diff --git a/your-code/challenge-1.ipynb b/your-code/[challenge-1] Bruno.ipynb
similarity index 69%
rename from your-code/challenge-1.ipynb
rename to your-code/[challenge-1] Bruno.ipynb
index 0808166..9742aa5 100644
--- a/your-code/challenge-1.ipynb
+++ b/your-code/[challenge-1] Bruno.ipynb	
@@ -66,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -82,6 +82,95 @@
     "    \"\"\""
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import nltk \n",
+    "from nltk.stem import WordNetLemmatizer\n",
+    "from nltk.corpus import stopwords\n",
+    "from sklearn.feature_extraction.text import CountVectorizer\n",
+    "#from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "from nltk.tokenize import word_tokenize \n",
+    "from nltk.stem import PorterStemmer, LancasterStemmer, SnowballStemmer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package stopwords to\n",
+      "[nltk_data]     C:\\Users\\btdjf\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package stopwords is already up-to-date!\n",
+      "[nltk_data] Downloading package wordnet to\n",
+      "[nltk_data]     C:\\Users\\btdjf\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package wordnet is already up-to-date!\n",
+      "[nltk_data] Downloading package punkt to\n",
+      "[nltk_data]     C:\\Users\\btdjf\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package punkt is already up-to-date!\n",
+      "[nltk_data] Downloading package omw-1.4 to\n",
+      "[nltk_data]     C:\\Users\\btdjf\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package omw-1.4 is already up-to-date!\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nltk.download('stopwords')\n",
+    "nltk.download('wordnet')\n",
+    "nltk.download('punkt') \n",
+    "nltk.download('omw-1.4')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ironhack s q website is\n"
+     ]
+    }
+   ],
+   "source": [
+    "import re\n",
+    "\n",
+    "text = \"@Ironhack's-#Q website 776-is http://ironhack.com [(2018)]\\\")\"\n",
+    "\n",
+    "def clean_up(text):\n",
+    "    # removing URLs\n",
+    "    text = re.sub(r\"http\\S+|www\\S+|https\\S+\", \"\", text)\n",
+    "    # removing special characters\n",
+    "    text = re.sub(r\"[^a-zA-Z ]+\", \" \", text)\n",
+    "    # converting to lowercase\n",
+    "    text = text.lower()\n",
+    "    # Remove extra whitespaces\n",
+    "    text = re.sub(r\"\\s+\", \" \", text)\n",
+    "    return text.strip()\n",
+    "\n",
+    "print(clean_up(text))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -101,7 +190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -117,6 +206,28 @@
     "    \"\"\""
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['ironhack', 's', 'q', 'website', 'is']\n"
+     ]
+    }
+   ],
+   "source": [
+    "text = \"ironhack s q website is\"\n",
+    "\n",
+    "def tokenize(text):\n",
+    "    return word_tokenize(text)\n",
+    "\n",
+    "print(tokenize(text))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -145,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -161,6 +272,37 @@
     "    \"\"\""
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['ironhack', 's', 'q', 'websit', 'is']\n"
+     ]
+    }
+   ],
+   "source": [
+    "words = ['ironhack', 's', 'q', 'website', 'is']\n",
+    "\n",
+    "def stem_and_lemmatize(words):\n",
+    "    stemmer = PorterStemmer()\n",
+    "    lemmatizer = WordNetLemmatizer()\n",
+    "    stemmed_and_lemmatized = []\n",
+    "\n",
+    "    for word in words:\n",
+    "        stemmed_word = stemmer.stem(word)\n",
+    "        lemmatized_word = lemmatizer.lemmatize(stemmed_word)\n",
+    "        stemmed_and_lemmatized.append((lemmatized_word))\n",
+    "\n",
+    "    return stemmed_and_lemmatized\n",
+    "\n",
+    "print(stem_and_lemmatize(words))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -176,7 +318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -192,6 +334,30 @@
     "    \"\"\""
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['ironhack', 'websit']\n"
+     ]
+    }
+   ],
+   "source": [
+    "words = ['ironhack', 's', 'q', 'websit', 'is']\n",
+    "\n",
+    "def remove_stopwords(words):\n",
+    "    stop_words = set(stopwords.words('english'))\n",
+    "    filtered_words = [word for word in words if word not in stop_words and len(word) > 1]\n",
+    "    return filtered_words\n",
+    "\n",
+    "print(remove_stopwords(words))\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -204,7 +370,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -218,7 +384,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,
diff --git a/your-code/[challenge-2] Bruno.ipynb b/your-code/[challenge-2] Bruno.ipynb
new file mode 100644
index 0000000..a0dc5c3
--- /dev/null
+++ b/your-code/[challenge-2] Bruno.ipynb	
@@ -0,0 +1,5069 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Challenge 2: Sentiment Analysis\n",
+    "\n",
+    "In this challenge we will learn sentiment analysis and practice performing sentiment analysis on Twitter tweets. \n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "Sentiment analysis is to *systematically identify, extract, quantify, and study affective states and subjective information* based on texts ([reference](https://en.wikipedia.org/wiki/Sentiment_analysis)). In simple words, it's to understand whether a person is happy or unhappy in producing the piece of text. Why we (or rather, companies) care about sentiment in texts? It's because by understanding the sentiments in texts, we will be able to know if our customers are happy or unhappy about our products and services. If they are unhappy, the subsequent action is to figure out what have caused the unhappiness and make improvements.\n",
+    "\n",
+    "Basic sentiment analysis only understands the *positive* or *negative* (sometimes *neutral* too) polarities of the sentiment. More advanced sentiment analysis will also consider dimensions such as agreement, subjectivity, confidence, irony, and so on. In this challenge we will conduct the basic positive vs negative sentiment analysis based on real Twitter tweets.\n",
+    "\n",
+    "NLTK comes with a [sentiment analysis package](https://www.nltk.org/api/nltk.sentiment.html). This package is great for dummies to perform sentiment analysis because it requires only the textual data to make predictions. For example:\n",
+    "\n",
+    "```python\n",
+    ">>> from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
+    ">>> txt = \"Ironhack is a Global Tech School ranked num 2 worldwide. 
",
+    "
",
+    "Our mission is to help people transform their careers and join a thriving community of tech professionals that love what they do.\"\n",
+    ">>> analyzer = SentimentIntensityAnalyzer()\n",
+    ">>> analyzer.polarity_scores(txt)\n",
+    "{'neg': 0.0, 'neu': 0.741, 'pos': 0.259, 'compound': 0.8442}\n",
+    "```\n",
+    "\n",
+    "In this challenge, however, you will not use NLTK's sentiment analysis package because in your Machine Learning training in the past 2 weeks you have learned how to make predictions more accurate than that. The [tweets data](https://www.kaggle.com/kazanova/sentiment140) we will be using today are already coded for the positive/negative sentiment. You will be able to use the Naïve Bayes classifier you learned in the lesson to predict the sentiment of tweets based on the labels."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conducting Sentiment Analysis\n",
+    "\n",
+    "### Loading and Exploring Data\n",
+    "\n",
+    "The dataset we'll be using today is located on Kaggle (https://www.kaggle.com/kazanova/sentiment140). Once you have downloaded and imported the dataset, it you will need to define the columns names: df.columns = ['target','id','date','flag','user','text']\n",
+    "\n",
+    "*Notes:* \n",
+    "\n",
+    "* The dataset is huuuuge (1.6m tweets). When you develop your data analysis codes, you can sample a subset of the data (e.g. 20k records) so that you will save a lot of time when you test your codes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>target</th>\n",
+       "      <th>id</th>\n",
+       "      <th>date</th>\n",
+       "      <th>flag</th>\n",
+       "      <th>user</th>\n",
+       "      <th>text</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1467810369</td>\n",
+       "      <td>Mon Apr 06 22:19:45 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>_TheSpecialOne_</td>\n",
+       "      <td>@switchfoot http://twitpic.com/2y1zl - Awww, t...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1467810672</td>\n",
+       "      <td>Mon Apr 06 22:19:49 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>scotthamilton</td>\n",
+       "      <td>is upset that he can't update his Facebook by ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1467810917</td>\n",
+       "      <td>Mon Apr 06 22:19:53 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>mattycus</td>\n",
+       "      <td>@Kenichan I dived many times for the ball. Man...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1467811184</td>\n",
+       "      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>ElleCTF</td>\n",
+       "      <td>my whole body feels itchy and like its on fire</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1467811193</td>\n",
+       "      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>Karoli</td>\n",
+       "      <td>@nationwideclass no, it's not behaving at all....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1599995</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2193601966</td>\n",
+       "      <td>Tue Jun 16 08:40:49 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>AmandaMarie1028</td>\n",
+       "      <td>Just woke up. Having no school is the best fee...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1599996</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2193601969</td>\n",
+       "      <td>Tue Jun 16 08:40:49 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>TheWDBoards</td>\n",
+       "      <td>TheWDB.com - Very cool to hear old Walt interv...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1599997</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2193601991</td>\n",
+       "      <td>Tue Jun 16 08:40:49 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>bpbabe</td>\n",
+       "      <td>Are you ready for your MoJo Makeover? Ask me f...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1599998</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2193602064</td>\n",
+       "      <td>Tue Jun 16 08:40:49 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>tinydiamondz</td>\n",
+       "      <td>Happy 38th Birthday to my boo of alll time!!! ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1599999</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2193602129</td>\n",
+       "      <td>Tue Jun 16 08:40:50 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>RyanTrevMorris</td>\n",
+       "      <td>happy #charitytuesday @theNSPCC @SparksCharity...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1600000 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         target          id                          date      flag  \\\n",
+       "0             0  1467810369  Mon Apr 06 22:19:45 PDT 2009  NO_QUERY   \n",
+       "1             0  1467810672  Mon Apr 06 22:19:49 PDT 2009  NO_QUERY   \n",
+       "2             0  1467810917  Mon Apr 06 22:19:53 PDT 2009  NO_QUERY   \n",
+       "3             0  1467811184  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY   \n",
+       "4             0  1467811193  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY   \n",
+       "...         ...         ...                           ...       ...   \n",
+       "1599995       4  2193601966  Tue Jun 16 08:40:49 PDT 2009  NO_QUERY   \n",
+       "1599996       4  2193601969  Tue Jun 16 08:40:49 PDT 2009  NO_QUERY   \n",
+       "1599997       4  2193601991  Tue Jun 16 08:40:49 PDT 2009  NO_QUERY   \n",
+       "1599998       4  2193602064  Tue Jun 16 08:40:49 PDT 2009  NO_QUERY   \n",
+       "1599999       4  2193602129  Tue Jun 16 08:40:50 PDT 2009  NO_QUERY   \n",
+       "\n",
+       "                    user                                               text  \n",
+       "0        _TheSpecialOne_  @switchfoot http://twitpic.com/2y1zl - Awww, t...  \n",
+       "1          scotthamilton  is upset that he can't update his Facebook by ...  \n",
+       "2               mattycus  @Kenichan I dived many times for the ball. Man...  \n",
+       "3                ElleCTF    my whole body feels itchy and like its on fire   \n",
+       "4                 Karoli  @nationwideclass no, it's not behaving at all....  \n",
+       "...                  ...                                                ...  \n",
+       "1599995  AmandaMarie1028  Just woke up. Having no school is the best fee...  \n",
+       "1599996      TheWDBoards  TheWDB.com - Very cool to hear old Walt interv...  \n",
+       "1599997           bpbabe  Are you ready for your MoJo Makeover? Ask me f...  \n",
+       "1599998     tinydiamondz  Happy 38th Birthday to my boo of alll time!!! ...  \n",
+       "1599999   RyanTrevMorris  happy #charitytuesday @theNSPCC @SparksCharity...  \n",
+       "\n",
+       "[1600000 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "column_names = ['target', 'id', 'date', 'flag', 'user', 'text']\n",
+    "\n",
+    "data = pd.read_csv(r'C:\\Users\\btdjf\\Desktop\\Ironhack 2\\Lab NLP\\lab-nlp\\your-code/training.1600000.processed.noemoticon.csv', names=column_names, encoding='latin-1')\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>target</th>\n",
+       "      <th>id</th>\n",
+       "      <th>date</th>\n",
+       "      <th>flag</th>\n",
+       "      <th>user</th>\n",
+       "      <th>text</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1495821</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2069999955</td>\n",
+       "      <td>Sun Jun 07 16:55:18 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>yokomarie510</td>\n",
+       "      <td>On my way up to [his] job, he called talk`n bo...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2388</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1468367488</td>\n",
+       "      <td>Tue Apr 07 01:14:20 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>joacimhagg</td>\n",
+       "      <td>@theresev solen var inge varm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1227054</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1990937963</td>\n",
+       "      <td>Mon Jun 01 06:04:20 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>Kumiyo_</td>\n",
+       "      <td>In one of those sick moods  yaaaaayyy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>534907</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2197731667</td>\n",
+       "      <td>Tue Jun 16 15:04:56 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>linnanm</td>\n",
+       "      <td>Got home from Trondheim 2night! We had so much...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>626580</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2230639116</td>\n",
+       "      <td>Thu Jun 18 17:23:49 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>elchupahueso</td>\n",
+       "      <td>Got a B in my skills class.  Very disappointed...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1314935</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2013971796</td>\n",
+       "      <td>Wed Jun 03 00:05:26 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>azaxacavabanama</td>\n",
+       "      <td>@iruleatwork the hindi one i guess. was browsi...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>367932</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2049351547</td>\n",
+       "      <td>Fri Jun 05 16:43:48 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>MarkSheppard</td>\n",
+       "      <td>@ElaineGiles We don't buy our Macs, we just re...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>79158</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1751707334</td>\n",
+       "      <td>Sat May 09 20:05:03 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>Joannethecat</td>\n",
+       "      <td>@roccoman so sad  please be safe, me luvs U ro...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1550493</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2183795447</td>\n",
+       "      <td>Mon Jun 15 14:59:51 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>a_l_wells</td>\n",
+       "      <td>mock the week baybieeee</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>355324</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2043889856</td>\n",
+       "      <td>Fri Jun 05 08:33:50 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>jfmanzo</td>\n",
+       "      <td>PLEASE go to http://krupskupofexcellence.com/ ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>20000 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         target          id                          date      flag  \\\n",
+       "1495821       4  2069999955  Sun Jun 07 16:55:18 PDT 2009  NO_QUERY   \n",
+       "2388          0  1468367488  Tue Apr 07 01:14:20 PDT 2009  NO_QUERY   \n",
+       "1227054       4  1990937963  Mon Jun 01 06:04:20 PDT 2009  NO_QUERY   \n",
+       "534907        0  2197731667  Tue Jun 16 15:04:56 PDT 2009  NO_QUERY   \n",
+       "626580        0  2230639116  Thu Jun 18 17:23:49 PDT 2009  NO_QUERY   \n",
+       "...         ...         ...                           ...       ...   \n",
+       "1314935       4  2013971796  Wed Jun 03 00:05:26 PDT 2009  NO_QUERY   \n",
+       "367932        0  2049351547  Fri Jun 05 16:43:48 PDT 2009  NO_QUERY   \n",
+       "79158         0  1751707334  Sat May 09 20:05:03 PDT 2009  NO_QUERY   \n",
+       "1550493       4  2183795447  Mon Jun 15 14:59:51 PDT 2009  NO_QUERY   \n",
+       "355324        0  2043889856  Fri Jun 05 08:33:50 PDT 2009  NO_QUERY   \n",
+       "\n",
+       "                    user                                               text  \n",
+       "1495821     yokomarie510  On my way up to [his] job, he called talk`n bo...  \n",
+       "2388          joacimhagg                     @theresev solen var inge varm   \n",
+       "1227054          Kumiyo_              In one of those sick moods  yaaaaayyy  \n",
+       "534907           linnanm  Got home from Trondheim 2night! We had so much...  \n",
+       "626580      elchupahueso  Got a B in my skills class.  Very disappointed...  \n",
+       "...                  ...                                                ...  \n",
+       "1314935  azaxacavabanama  @iruleatwork the hindi one i guess. was browsi...  \n",
+       "367932      MarkSheppard  @ElaineGiles We don't buy our Macs, we just re...  \n",
+       "79158       Joannethecat  @roccoman so sad  please be safe, me luvs U ro...  \n",
+       "1550493        a_l_wells                           mock the week baybieeee   \n",
+       "355324           jfmanzo  PLEASE go to http://krupskupofexcellence.com/ ...  \n",
+       "\n",
+       "[20000 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = data.sample(n=20000)\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Prepare Textual Data for Sentiment Analysis\n",
+    "\n",
+    "Now, apply the functions you have written in Challenge 1 to your whole data set. These functions include:\n",
+    "\n",
+    "* `clean_up()`\n",
+    "\n",
+    "* `tokenize()`\n",
+    "\n",
+    "* `stem_and_lemmatize()`\n",
+    "\n",
+    "* `remove_stopwords()`\n",
+    "\n",
+    "Create a new column called `text_processed` in the dataframe to contain the processed data. At the end, your `text_processed` column should contain lists of word tokens that are cleaned up. Your data should look like below:\n",
+    "\n",
+    "![Processed Data](data-cleaning-results.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "from nltk.tokenize import word_tokenize\n",
+    "from nltk.stem import PorterStemmer, WordNetLemmatizer\n",
+    "from nltk.corpus import stopwords"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def clean_up(text):\n",
+    "    cleaned_text = re.sub('[^a-zA-Z]', ' ', text)\n",
+    "    cleaned_text = re.sub(r'http\\S+', '', cleaned_text)\n",
+    "    cleaned_text = cleaned_text.lower()\n",
+    "    return cleaned_text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def tokenize(text):\n",
+    "    tokens = word_tokenize(text)\n",
+    "    return tokens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def stem_and_lemmatize(words):\n",
+    "    stemmer = PorterStemmer()\n",
+    "    lemmatizer = WordNetLemmatizer()\n",
+    "    stemmed_and_lemmatized = []\n",
+    "\n",
+    "    for word in words:\n",
+    "        stemmed_word = stemmer.stem(word)\n",
+    "        lemmatized_word = lemmatizer.lemmatize(stemmed_word)\n",
+    "        stemmed_and_lemmatized.append((lemmatized_word))\n",
+    "\n",
+    "    return stemmed_and_lemmatized"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def remove_stopwords(tokens):\n",
+    "    stop_words = set(stopwords.words('english'))\n",
+    "    filtered_tokens = [word for word in tokens if word not in stop_words and len(word) > 1]\n",
+    "    return filtered_tokens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['text_processed'] = data['text'].apply(lambda x: remove_stopwords(stem_and_lemmatize(tokenize(clean_up(x)))))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>target</th>\n",
+       "      <th>id</th>\n",
+       "      <th>date</th>\n",
+       "      <th>flag</th>\n",
+       "      <th>user</th>\n",
+       "      <th>text</th>\n",
+       "      <th>text_processed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1495821</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2069999955</td>\n",
+       "      <td>Sun Jun 07 16:55:18 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>yokomarie510</td>\n",
+       "      <td>On my way up to [his] job, he called talk`n bo...</td>\n",
+       "      <td>[way, hi, job, call, talk, bout, quot, momma, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2388</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1468367488</td>\n",
+       "      <td>Tue Apr 07 01:14:20 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>joacimhagg</td>\n",
+       "      <td>@theresev solen var inge varm</td>\n",
+       "      <td>[theresev, solen, var, ing, varm]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1227054</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1990937963</td>\n",
+       "      <td>Mon Jun 01 06:04:20 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>Kumiyo_</td>\n",
+       "      <td>In one of those sick moods  yaaaaayyy</td>\n",
+       "      <td>[one, sick, mood, yaaaaayyy]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>534907</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2197731667</td>\n",
+       "      <td>Tue Jun 16 15:04:56 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>linnanm</td>\n",
+       "      <td>Got home from Trondheim 2night! We had so much...</td>\n",
+       "      <td>[got, home, trondheim, night, much, fun, got, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>626580</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2230639116</td>\n",
+       "      <td>Thu Jun 18 17:23:49 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>elchupahueso</td>\n",
+       "      <td>Got a B in my skills class.  Very disappointed...</td>\n",
+       "      <td>[got, skill, class, veri, disappoint, mean, ne...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1314935</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2013971796</td>\n",
+       "      <td>Wed Jun 03 00:05:26 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>azaxacavabanama</td>\n",
+       "      <td>@iruleatwork the hindi one i guess. was browsi...</td>\n",
+       "      <td>[iruleatwork, hindi, one, guess, wa, brow, thr...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>367932</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2049351547</td>\n",
+       "      <td>Fri Jun 05 16:43:48 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>MarkSheppard</td>\n",
+       "      <td>@ElaineGiles We don't buy our Macs, we just re...</td>\n",
+       "      <td>[elainegil, buy, mac, rent, even, get, use, mu...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>79158</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1751707334</td>\n",
+       "      <td>Sat May 09 20:05:03 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>Joannethecat</td>\n",
+       "      <td>@roccoman so sad  please be safe, me luvs U ro...</td>\n",
+       "      <td>[roccoman, sad, plea, safe, luv, roccoman, hea...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1550493</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2183795447</td>\n",
+       "      <td>Mon Jun 15 14:59:51 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>a_l_wells</td>\n",
+       "      <td>mock the week baybieeee</td>\n",
+       "      <td>[mock, week, baybieee]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>355324</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2043889856</td>\n",
+       "      <td>Fri Jun 05 08:33:50 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>jfmanzo</td>\n",
+       "      <td>PLEASE go to http://krupskupofexcellence.com/ ...</td>\n",
+       "      <td>[plea, go, http, krupskupofexcel, com, vote, k...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>20000 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         target          id                          date      flag  \\\n",
+       "1495821       4  2069999955  Sun Jun 07 16:55:18 PDT 2009  NO_QUERY   \n",
+       "2388          0  1468367488  Tue Apr 07 01:14:20 PDT 2009  NO_QUERY   \n",
+       "1227054       4  1990937963  Mon Jun 01 06:04:20 PDT 2009  NO_QUERY   \n",
+       "534907        0  2197731667  Tue Jun 16 15:04:56 PDT 2009  NO_QUERY   \n",
+       "626580        0  2230639116  Thu Jun 18 17:23:49 PDT 2009  NO_QUERY   \n",
+       "...         ...         ...                           ...       ...   \n",
+       "1314935       4  2013971796  Wed Jun 03 00:05:26 PDT 2009  NO_QUERY   \n",
+       "367932        0  2049351547  Fri Jun 05 16:43:48 PDT 2009  NO_QUERY   \n",
+       "79158         0  1751707334  Sat May 09 20:05:03 PDT 2009  NO_QUERY   \n",
+       "1550493       4  2183795447  Mon Jun 15 14:59:51 PDT 2009  NO_QUERY   \n",
+       "355324        0  2043889856  Fri Jun 05 08:33:50 PDT 2009  NO_QUERY   \n",
+       "\n",
+       "                    user                                               text  \\\n",
+       "1495821     yokomarie510  On my way up to [his] job, he called talk`n bo...   \n",
+       "2388          joacimhagg                     @theresev solen var inge varm    \n",
+       "1227054          Kumiyo_              In one of those sick moods  yaaaaayyy   \n",
+       "534907           linnanm  Got home from Trondheim 2night! We had so much...   \n",
+       "626580      elchupahueso  Got a B in my skills class.  Very disappointed...   \n",
+       "...                  ...                                                ...   \n",
+       "1314935  azaxacavabanama  @iruleatwork the hindi one i guess. was browsi...   \n",
+       "367932      MarkSheppard  @ElaineGiles We don't buy our Macs, we just re...   \n",
+       "79158       Joannethecat  @roccoman so sad  please be safe, me luvs U ro...   \n",
+       "1550493        a_l_wells                           mock the week baybieeee    \n",
+       "355324           jfmanzo  PLEASE go to http://krupskupofexcellence.com/ ...   \n",
+       "\n",
+       "                                            text_processed  \n",
+       "1495821  [way, hi, job, call, talk, bout, quot, momma, ...  \n",
+       "2388                     [theresev, solen, var, ing, varm]  \n",
+       "1227054                       [one, sick, mood, yaaaaayyy]  \n",
+       "534907   [got, home, trondheim, night, much, fun, got, ...  \n",
+       "626580   [got, skill, class, veri, disappoint, mean, ne...  \n",
+       "...                                                    ...  \n",
+       "1314935  [iruleatwork, hindi, one, guess, wa, brow, thr...  \n",
+       "367932   [elainegil, buy, mac, rent, even, get, use, mu...  \n",
+       "79158    [roccoman, sad, plea, safe, luv, roccoman, hea...  \n",
+       "1550493                             [mock, week, baybieee]  \n",
+       "355324   [plea, go, http, krupskupofexcel, com, vote, k...  \n",
+       "\n",
+       "[20000 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating Bag of Words\n",
+    "\n",
+    "The purpose of this step is to create a [bag of words](https://en.wikipedia.org/wiki/Bag-of-words_model) from the processed data. The bag of words contains all the unique words in your whole text body (a.k.a. *corpus*) with the number of occurrence of each word. It will allow you to understand which words are the most important features across the whole corpus.\n",
+    "\n",
+    "Also, you can imagine you will have a massive set of words. The less important words (i.e. those of very low number of occurrence) do not contribute much to the sentiment. Therefore, you only need to use the most important words to build your feature set in the next step. In our case, we will use the top 5,000 words with the highest frequency to build the features.\n",
+    "\n",
+    "In the cell below, combine all the words in `text_processed` and calculate the frequency distribution of all words. A convenient library to calculate the term frequency distribution is NLTK's `FreqDist` class ([documentation](https://www.nltk.org/api/nltk.html#module-nltk.probability)). Then select the top 5,000 words from the frequency distribution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nltk import FreqDist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['way',\n",
+       " 'hi',\n",
+       " 'job',\n",
+       " 'call',\n",
+       " 'talk',\n",
+       " 'bout',\n",
+       " 'quot',\n",
+       " 'momma',\n",
+       " 'bore',\n",
+       " 'quot',\n",
+       " 'sooo',\n",
+       " 'go',\n",
+       " 'hi',\n",
+       " 'entertain',\n",
+       " 'ili',\n",
+       " 'daddi',\n",
+       " 'theresev',\n",
+       " 'solen',\n",
+       " 'var',\n",
+       " 'ing',\n",
+       " 'varm',\n",
+       " 'one',\n",
+       " 'sick',\n",
+       " 'mood',\n",
+       " 'yaaaaayyy',\n",
+       " 'got',\n",
+       " 'home',\n",
+       " 'trondheim',\n",
+       " 'night',\n",
+       " 'much',\n",
+       " 'fun',\n",
+       " 'got',\n",
+       " 'cold',\n",
+       " 'though',\n",
+       " 'got',\n",
+       " 'skill',\n",
+       " 'class',\n",
+       " 'veri',\n",
+       " 'disappoint',\n",
+       " 'mean',\n",
+       " 'need',\n",
+       " 'serious',\n",
+       " 'focu',\n",
+       " 'next',\n",
+       " 'quarter',\n",
+       " 'le',\n",
+       " 'zealotri',\n",
+       " 'knife',\n",
+       " 'skill',\n",
+       " 'wait',\n",
+       " 'till',\n",
+       " 'th',\n",
+       " 'june',\n",
+       " 'jonassss',\n",
+       " 'enjoy',\n",
+       " 'pre',\n",
+       " 'lucki',\n",
+       " 'peopl',\n",
+       " 'wait',\n",
+       " 'month',\n",
+       " 'befor',\n",
+       " 'sprint',\n",
+       " 'give',\n",
+       " 'enough',\n",
+       " 'discount',\n",
+       " 'upgrad',\n",
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+       " 'thank',\n",
+       " 'abcmsaj',\n",
+       " 'gon',\n",
+       " 'na',\n",
+       " 'back',\n",
+       " 'end',\n",
+       " 'juli',\n",
+       " 'easier',\n",
+       " 'nicer',\n",
+       " 'could',\n",
+       " 'ship',\n",
+       " 'go',\n",
+       " 'though',\n",
+       " 'snakob',\n",
+       " 'yup',\n",
+       " 'work',\n",
+       " 'weekend',\n",
+       " 'alway',\n",
+       " 'bummer',\n",
+       " 'gon',\n",
+       " 'na',\n",
+       " 'kick',\n",
+       " 'bowl',\n",
+       " 'tonit',\n",
+       " 'trishay',\n",
+       " 'rage',\n",
+       " 'pant',\n",
+       " 'rage',\n",
+       " 'glad',\n",
+       " 'see',\n",
+       " 'side',\n",
+       " 'would',\n",
+       " 'pheesh',\n",
+       " 'show',\n",
+       " 'woke',\n",
+       " 'shop',\n",
+       " 'today',\n",
+       " 'hope',\n",
+       " 'cuz',\n",
+       " 'desper',\n",
+       " 'need',\n",
+       " 'wardrob',\n",
+       " 'lookn',\n",
+       " 'kinda',\n",
+       " 'skimp',\n",
+       " 'got',\n",
+       " 'thing',\n",
+       " 'mind',\n",
+       " 'phase',\n",
+       " 'think',\n",
+       " 'adopt',\n",
+       " 'golden',\n",
+       " 'retriev',\n",
+       " 'miss',\n",
+       " 'gsd',\n",
+       " 'leilaboujnan',\n",
+       " 'ha',\n",
+       " 'beyond',\n",
+       " 'help',\n",
+       " 'serious',\n",
+       " 'bad',\n",
+       " 'thi',\n",
+       " 'guy',\n",
+       " 'http',\n",
+       " 'twitpic',\n",
+       " 'com',\n",
+       " 'nh',\n",
+       " 'jaredleto',\n",
+       " 'ha',\n",
+       " 'make',\n",
+       " 'better',\n",
+       " 'vampir',\n",
+       " 'xo',\n",
+       " 'fia',\n",
+       " 'mbase',\n",
+       " 'victorquest',\n",
+       " 'http',\n",
+       " 'twitpic',\n",
+       " 'com',\n",
+       " 'eep',\n",
+       " 'lizzi',\n",
+       " 'back',\n",
+       " 'littl',\n",
+       " 'late',\n",
+       " 'anyway',\n",
+       " 'yay',\n",
+       " 'love',\n",
+       " 'hair',\n",
+       " 'ticket',\n",
+       " 'lolla',\n",
+       " 'cost',\n",
+       " 'damn',\n",
+       " 'need',\n",
+       " 'way',\n",
+       " 'make',\n",
+       " 'money',\n",
+       " 'say',\n",
+       " 'helloooooo',\n",
+       " 'summervac',\n",
+       " 'littl',\n",
+       " 'miss',\n",
+       " 'sunshin',\n",
+       " 'giggl',\n",
+       " 'readyy',\n",
+       " 'great',\n",
+       " 'month',\n",
+       " 'aww',\n",
+       " 'gut',\n",
+       " 'didnt',\n",
+       " 'want',\n",
+       " 'either',\n",
+       " 'go',\n",
+       " 'last',\n",
+       " 'day',\n",
+       " 'juxt',\n",
+       " 'intern',\n",
+       " 'itsmeleighton',\n",
+       " 'wait',\n",
+       " 'hear',\n",
+       " 'nikkitabandita',\n",
+       " 'hey',\n",
+       " 'congrat',\n",
+       " 'graduat',\n",
+       " 'doe',\n",
+       " 'feel',\n",
+       " 'quot',\n",
+       " 'free',\n",
+       " 'last',\n",
+       " 'free',\n",
+       " 'last',\n",
+       " 'quot',\n",
+       " 'haha',\n",
+       " 'meredi',\n",
+       " 'hope',\n",
+       " 'treat',\n",
+       " 'hair',\n",
+       " 'way',\n",
+       " 'treat',\n",
+       " 'websit',\n",
+       " 'mileycyru',\n",
+       " 'http',\n",
+       " 'twitpic',\n",
+       " 'com',\n",
+       " 'dvj',\n",
+       " 'look',\n",
+       " 'fabul',\n",
+       " 'congrat',\n",
+       " 'award',\n",
+       " 'ili',\n",
+       " 'ulcer',\n",
+       " 'epicli',\n",
+       " 'lame',\n",
+       " 'onli',\n",
+       " 'imagin',\n",
+       " 'thi',\n",
+       " 'must',\n",
+       " 'feel',\n",
+       " 'like',\n",
+       " 'shot',\n",
+       " 'face',\n",
+       " 'close',\n",
+       " 'rang',\n",
+       " 'billyraycyru',\n",
+       " 'hi',\n",
+       " 'im',\n",
+       " ...]"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_words = [word for tokens in data['text_processed'] for word in tokens]\n",
+    "all_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FreqDist({'go': 1681, 'get': 1446, 'wa': 1330, 'day': 1312, 'thi': 1214, 'good': 1161, 'work': 1096, 'like': 1093, 'love': 1026, 'got': 964, ...})"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "freq_dist = FreqDist(all_words)\n",
+    "freq_dist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('go', 1681),\n",
+       " ('get', 1446),\n",
+       " ('wa', 1330),\n",
+       " ('day', 1312),\n",
+       " ('thi', 1214),\n",
+       " ('good', 1161),\n",
+       " ('work', 1096),\n",
+       " ('like', 1093),\n",
+       " ('love', 1026),\n",
+       " ('got', 964),\n",
+       " ('quot', 905),\n",
+       " ('http', 877),\n",
+       " ('today', 844),\n",
+       " ('one', 796),\n",
+       " ('time', 793),\n",
+       " ('lol', 768),\n",
+       " ('thank', 765),\n",
+       " ('back', 726),\n",
+       " ('miss', 714),\n",
+       " ('want', 687),\n",
+       " ('know', 679),\n",
+       " ('think', 668),\n",
+       " ('realli', 655),\n",
+       " ('feel', 635),\n",
+       " ('com', 634),\n",
+       " ('im', 627),\n",
+       " ('amp', 623),\n",
+       " ('see', 611),\n",
+       " ('night', 582),\n",
+       " ('well', 582),\n",
+       " ('need', 578),\n",
+       " ('na', 549),\n",
+       " ('oh', 545),\n",
+       " ('still', 540),\n",
+       " ('new', 529),\n",
+       " ('come', 528),\n",
+       " ('watch', 525),\n",
+       " ('hope', 524),\n",
+       " ('home', 518),\n",
+       " ('look', 511),\n",
+       " ('make', 500),\n",
+       " ('ha', 452),\n",
+       " ('twitter', 448),\n",
+       " ('last', 446),\n",
+       " ('much', 435),\n",
+       " ('morn', 433),\n",
+       " ('tomorrow', 423),\n",
+       " ('great', 417),\n",
+       " ('wait', 411),\n",
+       " ('sleep', 406),\n",
+       " ('haha', 403),\n",
+       " ('fun', 393),\n",
+       " ('wish', 383),\n",
+       " ('whi', 371),\n",
+       " ('sad', 367),\n",
+       " ('week', 353),\n",
+       " ('follow', 352),\n",
+       " ('right', 345),\n",
+       " ('tri', 345),\n",
+       " ('thing', 343),\n",
+       " ('friend', 338),\n",
+       " ('would', 338),\n",
+       " ('gon', 333),\n",
+       " ('onli', 331),\n",
+       " ('happi', 330),\n",
+       " ('sorri', 327),\n",
+       " ('say', 327),\n",
+       " ('way', 326),\n",
+       " ('tonight', 325),\n",
+       " ('bit', 324),\n",
+       " ('bad', 317),\n",
+       " ('nice', 310),\n",
+       " ('tweet', 308),\n",
+       " ('though', 307),\n",
+       " ('hi', 301),\n",
+       " ('veri', 300),\n",
+       " ('even', 297),\n",
+       " ('take', 292),\n",
+       " ('start', 286),\n",
+       " ('better', 283),\n",
+       " ('peopl', 276),\n",
+       " ('bed', 275),\n",
+       " ('yeah', 275),\n",
+       " ('could', 273),\n",
+       " ('hour', 271),\n",
+       " ('guy', 270),\n",
+       " ('school', 262),\n",
+       " ('play', 256),\n",
+       " ('use', 252),\n",
+       " ('show', 252),\n",
+       " ('hey', 248),\n",
+       " ('twitpic', 247),\n",
+       " ('rain', 246),\n",
+       " ('cant', 246),\n",
+       " ('awesom', 246),\n",
+       " ('ye', 240),\n",
+       " ('next', 238),\n",
+       " ('hate', 237),\n",
+       " ('soon', 236),\n",
+       " ('weekend', 233),\n",
+       " ('find', 231),\n",
+       " ('lt', 228),\n",
+       " ('never', 227),\n",
+       " ('let', 226),\n",
+       " ('sick', 225),\n",
+       " ('year', 220),\n",
+       " ('final', 216),\n",
+       " ('plea', 215),\n",
+       " ('yay', 213),\n",
+       " ('littl', 211),\n",
+       " ('ani', 210),\n",
+       " ('long', 204),\n",
+       " ('head', 203),\n",
+       " ('first', 203),\n",
+       " ('life', 203),\n",
+       " ('keep', 201),\n",
+       " ('girl', 201),\n",
+       " ('alway', 200),\n",
+       " ('movi', 200),\n",
+       " ('ok', 200),\n",
+       " ('alreadi', 198),\n",
+       " ('dont', 197),\n",
+       " ('phone', 195),\n",
+       " ('best', 195),\n",
+       " ('talk', 194),\n",
+       " ('bore', 193),\n",
+       " ('eat', 193),\n",
+       " ('befor', 192),\n",
+       " ('someth', 192),\n",
+       " ('wan', 192),\n",
+       " ('read', 190),\n",
+       " ('ever', 190),\n",
+       " ('listen', 190),\n",
+       " ('everyon', 190),\n",
+       " ('leav', 190),\n",
+       " ('sure', 189),\n",
+       " ('done', 188),\n",
+       " ('made', 185),\n",
+       " ('ly', 185),\n",
+       " ('man', 185),\n",
+       " ('anoth', 184),\n",
+       " ('tire', 184),\n",
+       " ('call', 183),\n",
+       " ('cool', 182),\n",
+       " ('went', 182),\n",
+       " ('hurt', 180),\n",
+       " ('help', 179),\n",
+       " ('song', 178),\n",
+       " ('yet', 178),\n",
+       " ('suck', 178),\n",
+       " ('mayb', 177),\n",
+       " ('lot', 175),\n",
+       " ('sound', 174),\n",
+       " ('ur', 173),\n",
+       " ('live', 171),\n",
+       " ('away', 170),\n",
+       " ('hous', 164),\n",
+       " ('guess', 163),\n",
+       " ('excit', 162),\n",
+       " ('pretti', 162),\n",
+       " ('finish', 161),\n",
+       " ('enjoy', 159),\n",
+       " ('someon', 159),\n",
+       " ('summer', 159),\n",
+       " ('thought', 159),\n",
+       " ('left', 157),\n",
+       " ('old', 155),\n",
+       " ('omg', 154),\n",
+       " ('babi', 154),\n",
+       " ('tell', 151),\n",
+       " ('parti', 150),\n",
+       " ('amaz', 149),\n",
+       " ('big', 149),\n",
+       " ('two', 148),\n",
+       " ('pic', 148),\n",
+       " ('earli', 147),\n",
+       " ('hear', 146),\n",
+       " ('put', 146),\n",
+       " ('becaus', 146),\n",
+       " ('end', 144),\n",
+       " ('readi', 143),\n",
+       " ('th', 142),\n",
+       " ('lost', 142),\n",
+       " ('check', 142),\n",
+       " ('ya', 141),\n",
+       " ('doe', 141),\n",
+       " ('mean', 139),\n",
+       " ('wow', 139),\n",
+       " ('hot', 138),\n",
+       " ('kid', 137),\n",
+       " ('yesterday', 137),\n",
+       " ('god', 137),\n",
+       " ('sunday', 136),\n",
+       " ('updat', 136),\n",
+       " ('saw', 135),\n",
+       " ('glad', 133),\n",
+       " ('run', 133),\n",
+       " ('book', 133),\n",
+       " ('stuff', 132),\n",
+       " ('later', 131),\n",
+       " ('shop', 131),\n",
+       " ('game', 130),\n",
+       " ('car', 129),\n",
+       " ('music', 129),\n",
+       " ('ugh', 128),\n",
+       " ('sun', 128),\n",
+       " ('sinc', 128),\n",
+       " ('job', 127),\n",
+       " ('stop', 127),\n",
+       " ('ta', 127),\n",
+       " ('iphon', 127),\n",
+       " ('hard', 126),\n",
+       " ('happen', 125),\n",
+       " ('actual', 125),\n",
+       " ('weather', 125),\n",
+       " ('give', 123),\n",
+       " ('stay', 123),\n",
+       " ('must', 123),\n",
+       " ('wonder', 123),\n",
+       " ('www', 122),\n",
+       " ('world', 122),\n",
+       " ('mom', 122),\n",
+       " ('monday', 122),\n",
+       " ('also', 121),\n",
+       " ('exam', 121),\n",
+       " ('might', 120),\n",
+       " ('beauti', 120),\n",
+       " ('post', 120),\n",
+       " ('seem', 120),\n",
+       " ('video', 119),\n",
+       " ('noth', 118),\n",
+       " ('fuck', 117),\n",
+       " ('ask', 117),\n",
+       " ('damn', 117),\n",
+       " ('birthday', 116),\n",
+       " ('buy', 116),\n",
+       " ('late', 115),\n",
+       " ('found', 115),\n",
+       " ('free', 115),\n",
+       " ('around', 113),\n",
+       " ('friday', 113),\n",
+       " ('total', 112),\n",
+       " ('cute', 112),\n",
+       " ('meet', 112),\n",
+       " ('drink', 111),\n",
+       " ('dad', 109),\n",
+       " ('sweet', 109),\n",
+       " ('woke', 108),\n",
+       " ('mani', 108),\n",
+       " ('said', 108),\n",
+       " ('cri', 108),\n",
+       " ('poor', 108),\n",
+       " ('till', 107),\n",
+       " ('boy', 107),\n",
+       " ('aww', 107),\n",
+       " ('clean', 106),\n",
+       " ('pictur', 106),\n",
+       " ('plan', 105),\n",
+       " ('luck', 104),\n",
+       " ('dinner', 103),\n",
+       " ('welcom', 103),\n",
+       " ('anyth', 102),\n",
+       " ('famili', 102),\n",
+       " ('without', 101),\n",
+       " ('busi', 101),\n",
+       " ('name', 100),\n",
+       " ('studi', 100),\n",
+       " ('face', 99),\n",
+       " ('place', 99),\n",
+       " ('may', 98),\n",
+       " ('least', 98),\n",
+       " ('win', 98),\n",
+       " ('lunch', 98),\n",
+       " ('gt', 98),\n",
+       " ('enough', 97),\n",
+       " ('funni', 96),\n",
+       " ('okay', 96),\n",
+       " ('food', 95),\n",
+       " ('cold', 94),\n",
+       " ('gone', 94),\n",
+       " ('tv', 94),\n",
+       " ('walk', 94),\n",
+       " ('eye', 93),\n",
+       " ('tho', 93),\n",
+       " ('hahaha', 93),\n",
+       " ('die', 93),\n",
+       " ('caus', 92),\n",
+       " ('drive', 92),\n",
+       " ('shit', 92),\n",
+       " ('turn', 91),\n",
+       " ('believ', 91),\n",
+       " ('money', 90),\n",
+       " ('wrong', 90),\n",
+       " ('ill', 90),\n",
+       " ('saturday', 90),\n",
+       " ('heart', 90),\n",
+       " ('sit', 90),\n",
+       " ('class', 89),\n",
+       " ('didnt', 89),\n",
+       " ('xx', 89),\n",
+       " ('real', 89),\n",
+       " ('hair', 89),\n",
+       " ('aw', 89),\n",
+       " ('mine', 89),\n",
+       " ('dream', 89),\n",
+       " ('repli', 88),\n",
+       " ('move', 88),\n",
+       " ('chang', 87),\n",
+       " ('forward', 87),\n",
+       " ('rock', 87),\n",
+       " ('month', 86),\n",
+       " ('stupid', 86),\n",
+       " ('beach', 86),\n",
+       " ('open', 86),\n",
+       " ('write', 86),\n",
+       " ('kinda', 85),\n",
+       " ('idea', 85),\n",
+       " ('outsid', 84),\n",
+       " ('tinyurl', 84),\n",
+       " ('brother', 84),\n",
+       " ('almost', 83),\n",
+       " ('came', 83),\n",
+       " ('wake', 83),\n",
+       " ('seen', 82),\n",
+       " ('word', 81),\n",
+       " ('whole', 81),\n",
+       " ('dog', 81),\n",
+       " ('hang', 81),\n",
+       " ('send', 80),\n",
+       " ('plurk', 80),\n",
+       " ('everyth', 80),\n",
+       " ('person', 80),\n",
+       " ('list', 80),\n",
+       " ('part', 79),\n",
+       " ('everi', 79),\n",
+       " ('probabl', 79),\n",
+       " ('anyon', 78),\n",
+       " ('onc', 78),\n",
+       " ('comput', 77),\n",
+       " ('goodnight', 77),\n",
+       " ('quit', 77),\n",
+       " ('fan', 77),\n",
+       " ('super', 77),\n",
+       " ('full', 76),\n",
+       " ('minut', 76),\n",
+       " ('offic', 76),\n",
+       " ('danc', 76),\n",
+       " ('trip', 76),\n",
+       " ('awww', 76),\n",
+       " ('true', 76),\n",
+       " ('pick', 75),\n",
+       " ('far', 75),\n",
+       " ('pain', 74),\n",
+       " ('room', 74),\n",
+       " ('facebook', 74),\n",
+       " ('favorit', 74),\n",
+       " ('coffe', 74),\n",
+       " ('news', 74),\n",
+       " ('took', 74),\n",
+       " ('headach', 73),\n",
+       " ('blog', 73),\n",
+       " ('pas', 73),\n",
+       " ('care', 73),\n",
+       " ('site', 72),\n",
+       " ('fail', 71),\n",
+       " ('mother', 71),\n",
+       " ('hello', 71),\n",
+       " ('boo', 71),\n",
+       " ('forgot', 71),\n",
+       " ('dude', 70),\n",
+       " ('bring', 70),\n",
+       " ('el', 70),\n",
+       " ('vote', 69),\n",
+       " ('la', 69),\n",
+       " ('hit', 69),\n",
+       " ('hug', 69),\n",
+       " ('learn', 69),\n",
+       " ('heard', 69),\n",
+       " ('lucki', 68),\n",
+       " ('pm', 68),\n",
+       " ('crazi', 68),\n",
+       " ('anymor', 68),\n",
+       " ('fall', 67),\n",
+       " ('ticket', 67),\n",
+       " ('ago', 67),\n",
+       " ('concert', 67),\n",
+       " ('st', 67),\n",
+       " ('test', 67),\n",
+       " ('hehe', 67),\n",
+       " ('break', 66),\n",
+       " ('sister', 66),\n",
+       " ('abl', 66),\n",
+       " ('text', 66),\n",
+       " ('worri', 66),\n",
+       " ('sooo', 65),\n",
+       " ('serious', 65),\n",
+       " ('lil', 65),\n",
+       " ('anyway', 65),\n",
+       " ('suppos', 65),\n",
+       " ('asleep', 65),\n",
+       " ('alon', 65),\n",
+       " ('shower', 65),\n",
+       " ('spend', 65),\n",
+       " ('rest', 64),\n",
+       " ('either', 64),\n",
+       " ('pay', 64),\n",
+       " ('interest', 64),\n",
+       " ('awak', 64),\n",
+       " ('wear', 63),\n",
+       " ('cuz', 63),\n",
+       " ('photo', 63),\n",
+       " ('visit', 63),\n",
+       " ('smile', 63),\n",
+       " ('fine', 63),\n",
+       " ('mind', 62),\n",
+       " ('internet', 62),\n",
+       " ('album', 62),\n",
+       " ('instead', 62),\n",
+       " ('rememb', 61),\n",
+       " ('cat', 61),\n",
+       " ('catch', 61),\n",
+       " ('btw', 61),\n",
+       " ('email', 61),\n",
+       " ('ah', 61),\n",
+       " ('set', 61),\n",
+       " ('link', 61),\n",
+       " ('onlin', 61),\n",
+       " ('red', 60),\n",
+       " ('add', 60),\n",
+       " ('kind', 60),\n",
+       " ('mileycyru', 60),\n",
+       " ('train', 60),\n",
+       " ('hell', 60),\n",
+       " ('sign', 60),\n",
+       " ('agre', 59),\n",
+       " ('half', 59),\n",
+       " ('close', 59),\n",
+       " ('sunni', 59),\n",
+       " ('bout', 58),\n",
+       " ('june', 58),\n",
+       " ('ice', 58),\n",
+       " ('fix', 58),\n",
+       " ('top', 58),\n",
+       " ('ladi', 58),\n",
+       " ('min', 57),\n",
+       " ('season', 57),\n",
+       " ('wont', 56),\n",
+       " ('lmao', 56),\n",
+       " ('bought', 56),\n",
+       " ('hand', 56),\n",
+       " ('ride', 56),\n",
+       " ('dress', 56),\n",
+       " ('annoy', 55),\n",
+       " ('decid', 55),\n",
+       " ('short', 55),\n",
+       " ('blip', 55),\n",
+       " ('ipod', 55),\n",
+       " ('wed', 55),\n",
+       " ('kill', 55),\n",
+       " ('cut', 55),\n",
+       " ('nap', 54),\n",
+       " ('broke', 54),\n",
+       " ('co', 54),\n",
+       " ('second', 54),\n",
+       " ('scare', 54),\n",
+       " ('card', 54),\n",
+       " ('join', 54),\n",
+       " ('cook', 53),\n",
+       " ('fm', 53),\n",
+       " ('holiday', 53),\n",
+       " ('togeth', 53),\n",
+       " ('sometim', 53),\n",
+       " ('youtub', 53),\n",
+       " ('order', 53),\n",
+       " ('soo', 52),\n",
+       " ('offici', 52),\n",
+       " ('weird', 52),\n",
+       " ('band', 51),\n",
+       " ('nite', 51),\n",
+       " ('page', 51),\n",
+       " ('high', 51),\n",
+       " ('sore', 51),\n",
+       " ('problem', 51),\n",
+       " ('stori', 50),\n",
+       " ('tommcfli', 50),\n",
+       " ('save', 50),\n",
+       " ('reason', 50),\n",
+       " ('nd', 50),\n",
+       " ('laptop', 50),\n",
+       " ('side', 49),\n",
+       " ('graduat', 49),\n",
+       " ('yea', 49),\n",
+       " ('crap', 49),\n",
+       " ('told', 49),\n",
+       " ('question', 49),\n",
+       " ('goe', 48),\n",
+       " ('forget', 48),\n",
+       " ('throat', 48),\n",
+       " ('congrat', 48),\n",
+       " ('homework', 48),\n",
+       " ('relax', 48),\n",
+       " ('hmm', 48),\n",
+       " ('shirt', 48),\n",
+       " ('sim', 48),\n",
+       " ('bye', 48),\n",
+       " ('stuck', 48),\n",
+       " ('tuesday', 47),\n",
+       " ('til', 47),\n",
+       " ('pack', 47),\n",
+       " ('figur', 47),\n",
+       " ('gym', 47),\n",
+       " ('air', 47),\n",
+       " ('breakfast', 47),\n",
+       " ('broken', 47),\n",
+       " ('past', 47),\n",
+       " ('ive', 47),\n",
+       " ('worst', 47),\n",
+       " ('award', 46),\n",
+       " ('citi', 46),\n",
+       " ('park', 46),\n",
+       " ('dear', 46),\n",
+       " ('laugh', 46),\n",
+       " ('sigh', 46),\n",
+       " ('download', 46),\n",
+       " ('perfect', 46),\n",
+       " ('le', 45),\n",
+       " ('definit', 45),\n",
+       " ('afternoon', 45),\n",
+       " ('celebr', 45),\n",
+       " ('mad', 45),\n",
+       " ('david', 45),\n",
+       " ('sing', 44),\n",
+       " ('answer', 44),\n",
+       " ('slow', 44),\n",
+       " ('differ', 44),\n",
+       " ('fast', 44),\n",
+       " ('cours', 44),\n",
+       " ('hungri', 44),\n",
+       " ('church', 44),\n",
+       " ('lie', 44),\n",
+       " ('fb', 44),\n",
+       " ('luv', 44),\n",
+       " ('chocol', 44),\n",
+       " ('chanc', 43),\n",
+       " ('goin', 43),\n",
+       " ('record', 43),\n",
+       " ('comment', 43),\n",
+       " ('travel', 43),\n",
+       " ('dm', 43),\n",
+       " ('uk', 43),\n",
+       " ('moon', 43),\n",
+       " ('complet', 43),\n",
+       " ('sleepi', 42),\n",
+       " ('foot', 42),\n",
+       " ('ddlovato', 42),\n",
+       " ('mr', 42),\n",
+       " ('tea', 42),\n",
+       " ('manag', 42),\n",
+       " ('fli', 42),\n",
+       " ('easi', 42),\n",
+       " ('store', 42),\n",
+       " ('math', 42),\n",
+       " ('depress', 42),\n",
+       " ('jealou', 42),\n",
+       " ('line', 42),\n",
+       " ('cake', 41),\n",
+       " ('tour', 41),\n",
+       " ('freak', 41),\n",
+       " ('dead', 41),\n",
+       " ('understand', 41),\n",
+       " ('worth', 41),\n",
+       " ('team', 41),\n",
+       " ('plu', 41),\n",
+       " ('box', 41),\n",
+       " ('longer', 41),\n",
+       " ('account', 41),\n",
+       " ('ppl', 41),\n",
+       " ('water', 41),\n",
+       " ('moment', 41),\n",
+       " ('support', 41),\n",
+       " ('fell', 41),\n",
+       " ('mood', 40),\n",
+       " ('sadli', 40),\n",
+       " ('messag', 40),\n",
+       " ('rather', 40),\n",
+       " ('woman', 40),\n",
+       " ('black', 40),\n",
+       " ('upset', 40),\n",
+       " ('yep', 40),\n",
+       " ('version', 40),\n",
+       " ('three', 40),\n",
+       " ('episod', 40),\n",
+       " ('radio', 40),\n",
+       " ('unfortun', 40),\n",
+       " ('except', 40),\n",
+       " ('mum', 40),\n",
+       " ('cream', 39),\n",
+       " ('met', 39),\n",
+       " ('chat', 39),\n",
+       " ('begin', 39),\n",
+       " ('shot', 39),\n",
+       " ('mac', 39),\n",
+       " ('cousin', 39),\n",
+       " ('camp', 39),\n",
+       " ('share', 39),\n",
+       " ('app', 39),\n",
+       " ('load', 39),\n",
+       " ('appar', 39),\n",
+       " ('shame', 39),\n",
+       " ('leg', 39),\n",
+       " ('beer', 39),\n",
+       " ('jona', 38),\n",
+       " ('ad', 38),\n",
+       " ('colleg', 38),\n",
+       " ('kick', 38),\n",
+       " ('count', 38),\n",
+       " ('coupl', 38),\n",
+       " ('felt', 38),\n",
+       " ('point', 38),\n",
+       " ('nope', 38),\n",
+       " ('bum', 38),\n",
+       " ('ach', 38),\n",
+       " ('due', 37),\n",
+       " ('via', 37),\n",
+       " ('stress', 37),\n",
+       " ('hr', 37),\n",
+       " ('flight', 37),\n",
+       " ('father', 37),\n",
+       " ('babe', 37),\n",
+       " ('hubbi', 37),\n",
+       " ('design', 37),\n",
+       " ('ate', 37),\n",
+       " ('blue', 37),\n",
+       " ('bike', 37),\n",
+       " ('bitch', 37),\n",
+       " ('absolut', 37),\n",
+       " ('town', 37),\n",
+       " ('upload', 37),\n",
+       " ('bless', 37),\n",
+       " ('xd', 37),\n",
+       " ('burn', 36),\n",
+       " ('club', 36),\n",
+       " ('lay', 36),\n",
+       " ('special', 36),\n",
+       " ('flu', 36),\n",
+       " ('fantast', 36),\n",
+       " ('pool', 36),\n",
+       " ('warm', 36),\n",
+       " ('bet', 36),\n",
+       " ('son', 36),\n",
+       " ('lazi', 36),\n",
+       " ('soooo', 36),\n",
+       " ('ear', 36),\n",
+       " ('followfriday', 36),\n",
+       " ('power', 36),\n",
+       " ('forev', 36),\n",
+       " ('smell', 36),\n",
+       " ('lone', 35),\n",
+       " ('date', 35),\n",
+       " ('twit', 35),\n",
+       " ('vacat', 35),\n",
+       " ('wit', 35),\n",
+       " ('john', 35),\n",
+       " ('miley', 35),\n",
+       " ('swim', 35),\n",
+       " ('sort', 35),\n",
+       " ('cheer', 35),\n",
+       " ('wors', 35),\n",
+       " ('hotel', 35),\n",
+       " ('bodi', 35),\n",
+       " ('crash', 35),\n",
+       " ('usual', 35),\n",
+       " ('practic', 34),\n",
+       " ('wtf', 34),\n",
+       " ('knew', 34),\n",
+       " ('alright', 34),\n",
+       " ('type', 34),\n",
+       " ('yo', 34),\n",
+       " ('horribl', 34),\n",
+       " ('plane', 34),\n",
+       " ('road', 34),\n",
+       " ('green', 34),\n",
+       " ('meant', 34),\n",
+       " ('yummi', 34),\n",
+       " ('realiz', 34),\n",
+       " ('myspac', 34),\n",
+       " ('english', 34),\n",
+       " ('ju', 34),\n",
+       " ('shoot', 34),\n",
+       " ('connect', 34),\n",
+       " ('websit', 33),\n",
+       " ('cover', 33),\n",
+       " ('dvd', 33),\n",
+       " ('product', 33),\n",
+       " ('juli', 33),\n",
+       " ('safe', 33),\n",
+       " ('ahhh', 33),\n",
+       " ('note', 33),\n",
+       " ('thx', 33),\n",
+       " ('beat', 33),\n",
+       " ('pull', 33),\n",
+       " ('woo', 33),\n",
+       " ('parent', 33),\n",
+       " ('sent', 33),\n",
+       " ('present', 33),\n",
+       " ('art', 33),\n",
+       " ('prob', 33),\n",
+       " ('cd', 33),\n",
+       " ('appl', 33),\n",
+       " ('film', 33),\n",
+       " ('bbq', 33),\n",
+       " ('fact', 33),\n",
+       " ('stomach', 32),\n",
+       " ('drop', 32),\n",
+       " ('chees', 32),\n",
+       " ('yup', 32),\n",
+       " ('sunshin', 32),\n",
+       " ('child', 32),\n",
+       " ('tom', 32),\n",
+       " ('sat', 32),\n",
+       " ('remind', 32),\n",
+       " ('histori', 32),\n",
+       " ('sold', 32),\n",
+       " ('paper', 32),\n",
+       " ('bird', 32),\n",
+       " ('addict', 32),\n",
+       " ('tear', 32),\n",
+       " ('doctor', 32),\n",
+       " ('doesnt', 32),\n",
+       " ('ahh', 32),\n",
+       " ('step', 32),\n",
+       " ('inspir', 32),\n",
+       " ('star', 32),\n",
+       " ('fair', 32),\n",
+       " ('disappoint', 31),\n",
+       " ('terribl', 31),\n",
+       " ('mtv', 31),\n",
+       " ('da', 31),\n",
+       " ('spent', 31),\n",
+       " ('white', 31),\n",
+       " ('especi', 31),\n",
+       " ('pink', 31),\n",
+       " ('garden', 31),\n",
+       " ('lose', 31),\n",
+       " ('interview', 31),\n",
+       " ('laker', 31),\n",
+       " ('idk', 31),\n",
+       " ('shoe', 31),\n",
+       " ('allow', 31),\n",
+       " ('alot', 31),\n",
+       " ('tummi', 31),\n",
+       " ('appreci', 31),\n",
+       " ('block', 31),\n",
+       " ('goodby', 31),\n",
+       " ('watchin', 31),\n",
+       " ('dark', 31),\n",
+       " ('hahah', 31),\n",
+       " ('bar', 31),\n",
+       " ('door', 31),\n",
+       " ('bb', 31),\n",
+       " ('gd', 31),\n",
+       " ('london', 31),\n",
+       " ('cancel', 31),\n",
+       " ('project', 30),\n",
+       " ('surpris', 30),\n",
+       " ('promis', 30),\n",
+       " ('stand', 30),\n",
+       " ('yr', 30),\n",
+       " ('angel', 30),\n",
+       " ('cooki', 30),\n",
+       " ('chill', 30),\n",
+       " ('land', 30),\n",
+       " ('dure', 30),\n",
+       " ('event', 30),\n",
+       " ('havent', 30),\n",
+       " ('storm', 30),\n",
+       " ('background', 30),\n",
+       " ('airport', 30),\n",
+       " ('si', 30),\n",
+       " ('edit', 29),\n",
+       " ('mention', 29),\n",
+       " ('number', 29),\n",
+       " ('futur', 29),\n",
+       " ('becom', 29),\n",
+       " ('possibl', 29),\n",
+       " ('revis', 29),\n",
+       " ('ouch', 29),\n",
+       " ('extra', 29),\n",
+       " ('slept', 29),\n",
+       " ('light', 29),\n",
+       " ('window', 29),\n",
+       " ('age', 29),\n",
+       " ('boyfriend', 29),\n",
+       " ('act', 29),\n",
+       " ('small', 28),\n",
+       " ('bu', 28),\n",
+       " ('gave', 28),\n",
+       " ('demi', 28),\n",
+       " ('current', 28),\n",
+       " ('mommi', 28),\n",
+       " ('mess', 28),\n",
+       " ('uh', 28),\n",
+       " ('channel', 28),\n",
+       " ('search', 28),\n",
+       " ('sale', 28),\n",
+       " ('price', 28),\n",
+       " ('fri', 28),\n",
+       " ('touch', 28),\n",
+       " ('ball', 28),\n",
+       " ('chicken', 28),\n",
+       " ('finger', 28),\n",
+       " ('huh', 28),\n",
+       " ('fight', 28),\n",
+       " ('mail', 28),\n",
+       " ('front', 28),\n",
+       " ('web', 28),\n",
+       " ('voic', 28),\n",
+       " ('piss', 28),\n",
+       " ('daddi', 27),\n",
+       " ('eh', 27),\n",
+       " ('chip', 27),\n",
+       " ('recommend', 27),\n",
+       " ('wine', 27),\n",
+       " ('tr', 27),\n",
+       " ('puppi', 27),\n",
+       " ('street', 27),\n",
+       " ('scari', 27),\n",
+       " ('arm', 27),\n",
+       " ('hmmm', 27),\n",
+       " ('expect', 27),\n",
+       " ('twilight', 27),\n",
+       " ('kitti', 27),\n",
+       " ('round', 27),\n",
+       " ('bug', 27),\n",
+       " ('pizza', 27),\n",
+       " ('itun', 27),\n",
+       " ('king', 27),\n",
+       " ('huge', 27),\n",
+       " ('coz', 27),\n",
+       " ('color', 27),\n",
+       " ('kati', 27),\n",
+       " ('confus', 27),\n",
+       " ('speak', 27),\n",
+       " ('tip', 27),\n",
+       " ('xxx', 27),\n",
+       " ('jonasbroth', 26),\n",
+       " ('bag', 26),\n",
+       " ('nick', 26),\n",
+       " ('xo', 26),\n",
+       " ('ny', 26),\n",
+       " ('kate', 26),\n",
+       " ('caught', 26),\n",
+       " ('hospit', 26),\n",
+       " ('deserv', 26),\n",
+       " ('gay', 26),\n",
+       " ('xoxo', 26),\n",
+       " ('nobodi', 26),\n",
+       " ('bday', 26),\n",
+       " ('wast', 26),\n",
+       " ('servic', 26),\n",
+       " ('invit', 26),\n",
+       " ('id', 26),\n",
+       " ('hill', 26),\n",
+       " ('offer', 26),\n",
+       " ('low', 25),\n",
+       " ('along', 25),\n",
+       " ('thursday', 25),\n",
+       " ('kiss', 25),\n",
+       " ('jb', 25),\n",
+       " ('exhaust', 25),\n",
+       " ('drunk', 25),\n",
+       " ('impress', 25),\n",
+       " ('rip', 25),\n",
+       " ('code', 25),\n",
+       " ('paint', 25),\n",
+       " ('em', 25),\n",
+       " ('matter', 25),\n",
+       " ('dunno', 25),\n",
+       " ('jon', 25),\n",
+       " ('mile', 25),\n",
+       " ('wednesday', 25),\n",
+       " ('blood', 25),\n",
+       " ('ahead', 25),\n",
+       " ('googl', 25),\n",
+       " ('avail', 25),\n",
+       " ('england', 25),\n",
+       " ('case', 25),\n",
+       " ('exactli', 25),\n",
+       " ('hun', 25),\n",
+       " ('wash', 25),\n",
+       " ('magic', 25),\n",
+       " ('delet', 25),\n",
+       " ('tast', 25),\n",
+       " ('wet', 25),\n",
+       " ('notic', 25),\n",
+       " ('proud', 25),\n",
+       " ('jame', 25),\n",
+       " ('fire', 25),\n",
+       " ('continu', 24),\n",
+       " ('non', 24),\n",
+       " ('countri', 24),\n",
+       " ('vip', 24),\n",
+       " ('taylor', 24),\n",
+       " ('earlier', 24),\n",
+       " ('chri', 24),\n",
+       " ('kno', 24),\n",
+       " ('ruin', 24),\n",
+       " ('direct', 24),\n",
+       " ('arriv', 24),\n",
+       " ('heat', 24),\n",
+       " ('prepar', 24),\n",
+       " ('fit', 24),\n",
+       " ('pray', 24),\n",
+       " ('doubt', 24),\n",
+       " ('hold', 24),\n",
+       " ('race', 24),\n",
+       " ('everybodi', 24),\n",
+       " ('fav', 24),\n",
+       " ('daughter', 24),\n",
+       " ('track', 24),\n",
+       " ('honey', 24),\n",
+       " ('troubl', 24),\n",
+       " ('taken', 24),\n",
+       " ('brain', 24),\n",
+       " ('tune', 24),\n",
+       " ('consid', 24),\n",
+       " ('bro', 24),\n",
+       " ('net', 24),\n",
+       " ('joe', 24),\n",
+       " ('husband', 24),\n",
+       " ('sky', 24),\n",
+       " ('shut', 23),\n",
+       " ('tonit', 23),\n",
+       " ('normal', 23),\n",
+       " ('gorgeou', 23),\n",
+       " ('apart', 23),\n",
+       " ('ff', 23),\n",
+       " ('choic', 23),\n",
+       " ('etc', 23),\n",
+       " ('jordanknight', 23),\n",
+       " ('somewher', 23),\n",
+       " ('behind', 23),\n",
+       " ('lesson', 23),\n",
+       " ('fill', 23),\n",
+       " ('camera', 23),\n",
+       " ('nail', 23),\n",
+       " ('nah', 23),\n",
+       " ('cross', 23),\n",
+       " ('couldnt', 23),\n",
+       " ('ohh', 23),\n",
+       " ('delici', 23),\n",
+       " ('instal', 23),\n",
+       " ('secret', 23),\n",
+       " ('screen', 23),\n",
+       " ('marri', 23),\n",
+       " ('forc', 23),\n",
+       " ('hangov', 23),\n",
+       " ('issu', 23),\n",
+       " ('paul', 23),\n",
+       " ('darn', 23),\n",
+       " ('profil', 23),\n",
+       " ('group', 23),\n",
+       " ('suggest', 23),\n",
+       " ('aint', 23),\n",
+       " ('memori', 23),\n",
+       " ('tweep', 23),\n",
+       " ('spam', 23),\n",
+       " ('winter', 23),\n",
+       " ('smoke', 22),\n",
+       " ('sam', 22),\n",
+       " ('compani', 22),\n",
+       " ('ryan', 22),\n",
+       " ('self', 22),\n",
+       " ('hannah', 22),\n",
+       " ('bath', 22),\n",
+       " ('prefer', 22),\n",
+       " ('sweeti', 22),\n",
+       " ('death', 22),\n",
+       " ('blow', 22),\n",
+       " ('shall', 22),\n",
+       " ('screw', 22),\n",
+       " ('often', 22),\n",
+       " ('dat', 22),\n",
+       " ('system', 22),\n",
+       " ('disney', 22),\n",
+       " ('state', 22),\n",
+       " ('sa', 22),\n",
+       " ('result', 22),\n",
+       " ('peac', 22),\n",
+       " ('bunch', 22),\n",
+       " ('creat', 22),\n",
+       " ('american', 22),\n",
+       " ('sushi', 22),\n",
+       " ('adam', 22),\n",
+       " ('nyc', 22),\n",
+       " ('ooh', 22),\n",
+       " ('crappi', 22),\n",
+       " ('mo', 22),\n",
+       " ('experi', 22),\n",
+       " ('dang', 22),\n",
+       " ...]"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "top_words = freq_dist.most_common(5000)\n",
+    "top_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['go',\n",
+       " 'get',\n",
+       " 'wa',\n",
+       " 'day',\n",
+       " 'thi',\n",
+       " 'good',\n",
+       " 'work',\n",
+       " 'like',\n",
+       " 'love',\n",
+       " 'got',\n",
+       " 'quot',\n",
+       " 'http',\n",
+       " 'today',\n",
+       " 'one',\n",
+       " 'time',\n",
+       " 'lol',\n",
+       " 'thank',\n",
+       " 'back',\n",
+       " 'miss',\n",
+       " 'want',\n",
+       " 'know',\n",
+       " 'think',\n",
+       " 'realli',\n",
+       " 'feel',\n",
+       " 'com',\n",
+       " 'im',\n",
+       " 'amp',\n",
+       " 'see',\n",
+       " 'night',\n",
+       " 'well',\n",
+       " 'need',\n",
+       " 'na',\n",
+       " 'oh',\n",
+       " 'still',\n",
+       " 'new',\n",
+       " 'come',\n",
+       " 'watch',\n",
+       " 'hope',\n",
+       " 'home',\n",
+       " 'look',\n",
+       " 'make',\n",
+       " 'ha',\n",
+       " 'twitter',\n",
+       " 'last',\n",
+       " 'much',\n",
+       " 'morn',\n",
+       " 'tomorrow',\n",
+       " 'great',\n",
+       " 'wait',\n",
+       " 'sleep',\n",
+       " 'haha',\n",
+       " 'fun',\n",
+       " 'wish',\n",
+       " 'whi',\n",
+       " 'sad',\n",
+       " 'week',\n",
+       " 'follow',\n",
+       " 'right',\n",
+       " 'tri',\n",
+       " 'thing',\n",
+       " 'friend',\n",
+       " 'would',\n",
+       " 'gon',\n",
+       " 'onli',\n",
+       " 'happi',\n",
+       " 'sorri',\n",
+       " 'say',\n",
+       " 'way',\n",
+       " 'tonight',\n",
+       " 'bit',\n",
+       " 'bad',\n",
+       " 'nice',\n",
+       " 'tweet',\n",
+       " 'though',\n",
+       " 'hi',\n",
+       " 'veri',\n",
+       " 'even',\n",
+       " 'take',\n",
+       " 'start',\n",
+       " 'better',\n",
+       " 'peopl',\n",
+       " 'bed',\n",
+       " 'yeah',\n",
+       " 'could',\n",
+       " 'hour',\n",
+       " 'guy',\n",
+       " 'school',\n",
+       " 'play',\n",
+       " 'use',\n",
+       " 'show',\n",
+       " 'hey',\n",
+       " 'twitpic',\n",
+       " 'rain',\n",
+       " 'cant',\n",
+       " 'awesom',\n",
+       " 'ye',\n",
+       " 'next',\n",
+       " 'hate',\n",
+       " 'soon',\n",
+       " 'weekend',\n",
+       " 'find',\n",
+       " 'lt',\n",
+       " 'never',\n",
+       " 'let',\n",
+       " 'sick',\n",
+       " 'year',\n",
+       " 'final',\n",
+       " 'plea',\n",
+       " 'yay',\n",
+       " 'littl',\n",
+       " 'ani',\n",
+       " 'long',\n",
+       " 'head',\n",
+       " 'first',\n",
+       " 'life',\n",
+       " 'keep',\n",
+       " 'girl',\n",
+       " 'alway',\n",
+       " 'movi',\n",
+       " 'ok',\n",
+       " 'alreadi',\n",
+       " 'dont',\n",
+       " 'phone',\n",
+       " 'best',\n",
+       " 'talk',\n",
+       " 'bore',\n",
+       " 'eat',\n",
+       " 'befor',\n",
+       " 'someth',\n",
+       " 'wan',\n",
+       " 'read',\n",
+       " 'ever',\n",
+       " 'listen',\n",
+       " 'everyon',\n",
+       " 'leav',\n",
+       " 'sure',\n",
+       " 'done',\n",
+       " 'made',\n",
+       " 'ly',\n",
+       " 'man',\n",
+       " 'anoth',\n",
+       " 'tire',\n",
+       " 'call',\n",
+       " 'cool',\n",
+       " 'went',\n",
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+       " 'honey',\n",
+       " 'troubl',\n",
+       " 'taken',\n",
+       " 'brain',\n",
+       " 'tune',\n",
+       " 'consid',\n",
+       " 'bro',\n",
+       " 'net',\n",
+       " 'joe',\n",
+       " 'husband',\n",
+       " 'sky',\n",
+       " 'shut',\n",
+       " 'tonit',\n",
+       " 'normal',\n",
+       " 'gorgeou',\n",
+       " 'apart',\n",
+       " 'ff',\n",
+       " 'choic',\n",
+       " 'etc',\n",
+       " 'jordanknight',\n",
+       " 'somewher',\n",
+       " 'behind',\n",
+       " 'lesson',\n",
+       " 'fill',\n",
+       " 'camera',\n",
+       " 'nail',\n",
+       " 'nah',\n",
+       " 'cross',\n",
+       " 'couldnt',\n",
+       " 'ohh',\n",
+       " 'delici',\n",
+       " 'instal',\n",
+       " 'secret',\n",
+       " 'screen',\n",
+       " 'marri',\n",
+       " 'forc',\n",
+       " 'hangov',\n",
+       " 'issu',\n",
+       " 'paul',\n",
+       " 'darn',\n",
+       " 'profil',\n",
+       " 'group',\n",
+       " 'suggest',\n",
+       " 'aint',\n",
+       " 'memori',\n",
+       " 'tweep',\n",
+       " 'spam',\n",
+       " 'winter',\n",
+       " 'smoke',\n",
+       " 'sam',\n",
+       " 'compani',\n",
+       " 'ryan',\n",
+       " 'self',\n",
+       " 'hannah',\n",
+       " 'bath',\n",
+       " 'prefer',\n",
+       " 'sweeti',\n",
+       " 'death',\n",
+       " 'blow',\n",
+       " 'shall',\n",
+       " 'screw',\n",
+       " 'often',\n",
+       " 'dat',\n",
+       " 'system',\n",
+       " 'disney',\n",
+       " 'state',\n",
+       " 'sa',\n",
+       " 'result',\n",
+       " 'peac',\n",
+       " 'bunch',\n",
+       " 'creat',\n",
+       " 'american',\n",
+       " 'sushi',\n",
+       " 'adam',\n",
+       " 'nyc',\n",
+       " 'ooh',\n",
+       " 'crappi',\n",
+       " 'mo',\n",
+       " 'experi',\n",
+       " 'dang',\n",
+       " ...]"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selected_words = [word for word, frequency in top_words]\n",
+    "selected_words"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Building Features\n",
+    "\n",
+    "Now let's build the features. Using the top 5,000 words, create a 2-dimensional matrix to record whether each of those words is contained in each document (tweet). Then you also have an output column to indicate whether the sentiment in each tweet is positive. For example, assuming your bag of words has 5 items (`['one', 'two', 'three', 'four', 'five']`) out of 4 documents (`['A', 'B', 'C', 'D']`), your feature set is essentially:\n",
+    "\n",
+    "| Doc | one | two | three | four | five | is_positive |\n",
+    "|---|---|---|---|---|---|---|\n",
+    "| A | True | False | False | True | False | True |\n",
+    "| B | False | False | False | True | True | False |\n",
+    "| C | False | True | False | False | False | True |\n",
+    "| D | True | False | False | False | True | False|\n",
+    "\n",
+    "However, because the `nltk.NaiveBayesClassifier.train` class we will use in the next step does not work with Pandas dataframe, the structure of your feature set should be converted to the Python list looking like below:\n",
+    "\n",
+    "```python\n",
+    "[\n",
+    "\t({\n",
+    "\t\t'one': True,\n",
+    "\t\t'two': False,\n",
+    "\t\t'three': False,\n",
+    "\t\t'four': True,\n",
+    "\t\t'five': False\n",
+    "\t}, True),\n",
+    "\t({\n",
+    "\t\t'one': False,\n",
+    "\t\t'two': False,\n",
+    "\t\t'three': False,\n",
+    "\t\t'four': True,\n",
+    "\t\t'five': True\n",
+    "\t}, False),\n",
+    "\t({\n",
+    "\t\t'one': False,\n",
+    "\t\t'two': True,\n",
+    "\t\t'three': False,\n",
+    "\t\t'four': False,\n",
+    "\t\t'five': False\n",
+    "\t}, True),\n",
+    "\t({\n",
+    "\t\t'one': True,\n",
+    "\t\t'two': False,\n",
+    "\t\t'three': False,\n",
+    "\t\t'four': False,\n",
+    "\t\t'five': True\n",
+    "\t}, False)\n",
+    "]\n",
+    "```\n",
+    "\n",
+    "To help you in this step, watch the [following video](https://www.youtube.com/watch?v=-vVskDsHcVc) to learn how to build the feature set with Python and NLTK. The source code in this video can be found [here](https://pythonprogramming.net/words-as-features-nltk-tutorial/)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![Building Features](building-features.jpg)](https://www.youtube.com/watch?v=-vVskDsHcVc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "({'go': True,\n",
+       "  'get': False,\n",
+       "  'wa': False,\n",
+       "  'day': False,\n",
+       "  'thi': False,\n",
+       "  'good': False,\n",
+       "  'work': False,\n",
+       "  'like': False,\n",
+       "  'love': False,\n",
+       "  'got': False,\n",
+       "  'quot': False,\n",
+       "  'http': True,\n",
+       "  'today': False,\n",
+       "  'one': False,\n",
+       "  'time': False,\n",
+       "  'lol': False,\n",
+       "  'thank': False,\n",
+       "  'back': False,\n",
+       "  'miss': False,\n",
+       "  'want': False,\n",
+       "  'know': False,\n",
+       "  'think': False,\n",
+       "  'realli': False,\n",
+       "  'feel': False,\n",
+       "  'com': True,\n",
+       "  'im': False,\n",
+       "  'amp': True,\n",
+       "  'see': False,\n",
+       "  'night': False,\n",
+       "  'well': False,\n",
+       "  'need': False,\n",
+       "  'na': False,\n",
+       "  'oh': False,\n",
+       "  'still': False,\n",
+       "  'new': False,\n",
+       "  'come': False,\n",
+       "  'watch': False,\n",
+       "  'hope': False,\n",
+       "  'home': False,\n",
+       "  'look': False,\n",
+       "  'make': False,\n",
+       "  'ha': False,\n",
+       "  'twitter': False,\n",
+       "  'last': False,\n",
+       "  'much': False,\n",
+       "  'morn': False,\n",
+       "  'tomorrow': False,\n",
+       "  'great': False,\n",
+       "  'wait': False,\n",
+       "  'sleep': False,\n",
+       "  'haha': False,\n",
+       "  'fun': False,\n",
+       "  'wish': False,\n",
+       "  'whi': False,\n",
+       "  'sad': False,\n",
+       "  'week': False,\n",
+       "  'follow': False,\n",
+       "  'right': False,\n",
+       "  'tri': False,\n",
+       "  'thing': False,\n",
+       "  'friend': False,\n",
+       "  'would': False,\n",
+       "  'gon': False,\n",
+       "  'onli': False,\n",
+       "  'happi': False,\n",
+       "  'sorri': False,\n",
+       "  'say': False,\n",
+       "  'way': False,\n",
+       "  'tonight': False,\n",
+       "  'bit': False,\n",
+       "  'bad': False,\n",
+       "  'nice': False,\n",
+       "  'tweet': False,\n",
+       "  'though': False,\n",
+       "  'hi': False,\n",
+       "  'veri': False,\n",
+       "  'even': False,\n",
+       "  'take': False,\n",
+       "  'start': False,\n",
+       "  'better': False,\n",
+       "  'peopl': False,\n",
+       "  'bed': False,\n",
+       "  'yeah': False,\n",
+       "  'could': False,\n",
+       "  'hour': False,\n",
+       "  'guy': False,\n",
+       "  'school': False,\n",
+       "  'play': False,\n",
+       "  'use': False,\n",
+       "  'show': False,\n",
+       "  'hey': False,\n",
+       "  'twitpic': False,\n",
+       "  'rain': False,\n",
+       "  'cant': False,\n",
+       "  'awesom': False,\n",
+       "  'ye': False,\n",
+       "  'next': False,\n",
+       "  'hate': False,\n",
+       "  'soon': False,\n",
+       "  'weekend': False,\n",
+       "  'find': False,\n",
+       "  'lt': False,\n",
+       "  'never': False,\n",
+       "  'let': False,\n",
+       "  'sick': False,\n",
+       "  'year': False,\n",
+       "  'final': False,\n",
+       "  'plea': True,\n",
+       "  'yay': False,\n",
+       "  'littl': False,\n",
+       "  'ani': False,\n",
+       "  'long': False,\n",
+       "  'head': False,\n",
+       "  'first': False,\n",
+       "  'life': False,\n",
+       "  'keep': False,\n",
+       "  'girl': False,\n",
+       "  'alway': False,\n",
+       "  'movi': False,\n",
+       "  'ok': False,\n",
+       "  'alreadi': False,\n",
+       "  'dont': False,\n",
+       "  'phone': False,\n",
+       "  'best': False,\n",
+       "  'talk': False,\n",
+       "  'bore': False,\n",
+       "  'eat': False,\n",
+       "  'befor': False,\n",
+       "  'someth': False,\n",
+       "  'wan': False,\n",
+       "  'read': False,\n",
+       "  'ever': False,\n",
+       "  'listen': False,\n",
+       "  'everyon': False,\n",
+       "  'leav': False,\n",
+       "  'sure': False,\n",
+       "  'done': False,\n",
+       "  'made': False,\n",
+       "  'ly': False,\n",
+       "  'man': False,\n",
+       "  'anoth': False,\n",
+       "  'tire': False,\n",
+       "  'call': False,\n",
+       "  'cool': False,\n",
+       "  'went': False,\n",
+       "  'hurt': False,\n",
+       "  'help': False,\n",
+       "  'song': False,\n",
+       "  'yet': False,\n",
+       "  'suck': False,\n",
+       "  'mayb': False,\n",
+       "  'lot': False,\n",
+       "  'sound': False,\n",
+       "  'ur': False,\n",
+       "  'live': False,\n",
+       "  'away': False,\n",
+       "  'hous': False,\n",
+       "  'guess': False,\n",
+       "  'excit': False,\n",
+       "  'pretti': False,\n",
+       "  'finish': False,\n",
+       "  'enjoy': False,\n",
+       "  'someon': False,\n",
+       "  'summer': False,\n",
+       "  'thought': False,\n",
+       "  'left': False,\n",
+       "  'old': False,\n",
+       "  'omg': False,\n",
+       "  'babi': False,\n",
+       "  'tell': False,\n",
+       "  'parti': False,\n",
+       "  'amaz': False,\n",
+       "  'big': False,\n",
+       "  'two': False,\n",
+       "  'pic': False,\n",
+       "  'earli': False,\n",
+       "  'hear': False,\n",
+       "  'put': False,\n",
+       "  'becaus': False,\n",
+       "  'end': False,\n",
+       "  'readi': False,\n",
+       "  'th': False,\n",
+       "  'lost': False,\n",
+       "  'check': False,\n",
+       "  'ya': False,\n",
+       "  'doe': False,\n",
+       "  'mean': False,\n",
+       "  'wow': False,\n",
+       "  'hot': False,\n",
+       "  'kid': False,\n",
+       "  'yesterday': False,\n",
+       "  'god': False,\n",
+       "  'sunday': False,\n",
+       "  'updat': False,\n",
+       "  'saw': False,\n",
+       "  'glad': False,\n",
+       "  'run': False,\n",
+       "  'book': False,\n",
+       "  'stuff': False,\n",
+       "  'later': False,\n",
+       "  'shop': False,\n",
+       "  'game': False,\n",
+       "  'car': False,\n",
+       "  'music': False,\n",
+       "  'ugh': False,\n",
+       "  'sun': False,\n",
+       "  'sinc': False,\n",
+       "  'job': False,\n",
+       "  'stop': False,\n",
+       "  'ta': False,\n",
+       "  'iphon': False,\n",
+       "  'hard': False,\n",
+       "  'happen': False,\n",
+       "  'actual': False,\n",
+       "  'weather': False,\n",
+       "  'give': False,\n",
+       "  'stay': False,\n",
+       "  'must': False,\n",
+       "  'wonder': False,\n",
+       "  'www': False,\n",
+       "  'world': False,\n",
+       "  'mom': False,\n",
+       "  'monday': False,\n",
+       "  'also': False,\n",
+       "  'exam': False,\n",
+       "  'might': False,\n",
+       "  'beauti': False,\n",
+       "  'post': False,\n",
+       "  'seem': False,\n",
+       "  'video': False,\n",
+       "  'noth': False,\n",
+       "  'fuck': False,\n",
+       "  'ask': False,\n",
+       "  'damn': False,\n",
+       "  'birthday': False,\n",
+       "  'buy': False,\n",
+       "  'late': False,\n",
+       "  'found': False,\n",
+       "  'free': False,\n",
+       "  'around': False,\n",
+       "  'friday': False,\n",
+       "  'total': False,\n",
+       "  'cute': False,\n",
+       "  'meet': False,\n",
+       "  'drink': False,\n",
+       "  'dad': False,\n",
+       "  'sweet': False,\n",
+       "  'woke': False,\n",
+       "  'mani': False,\n",
+       "  'said': False,\n",
+       "  'cri': False,\n",
+       "  'poor': False,\n",
+       "  'till': False,\n",
+       "  'boy': False,\n",
+       "  'aww': False,\n",
+       "  'clean': False,\n",
+       "  'pictur': False,\n",
+       "  'plan': False,\n",
+       "  'luck': False,\n",
+       "  'dinner': False,\n",
+       "  'welcom': False,\n",
+       "  'anyth': False,\n",
+       "  'famili': False,\n",
+       "  'without': False,\n",
+       "  'busi': False,\n",
+       "  'name': False,\n",
+       "  'studi': False,\n",
+       "  'face': False,\n",
+       "  'place': False,\n",
+       "  'may': False,\n",
+       "  'least': False,\n",
+       "  'win': True,\n",
+       "  'lunch': False,\n",
+       "  'gt': False,\n",
+       "  'enough': False,\n",
+       "  'funni': False,\n",
+       "  'okay': False,\n",
+       "  'food': False,\n",
+       "  'cold': False,\n",
+       "  'gone': False,\n",
+       "  'tv': False,\n",
+       "  'walk': False,\n",
+       "  'eye': False,\n",
+       "  'tho': False,\n",
+       "  'hahaha': False,\n",
+       "  'die': False,\n",
+       "  'caus': False,\n",
+       "  'drive': False,\n",
+       "  'shit': False,\n",
+       "  'turn': False,\n",
+       "  'believ': False,\n",
+       "  'money': False,\n",
+       "  'wrong': False,\n",
+       "  'ill': False,\n",
+       "  'saturday': False,\n",
+       "  'heart': False,\n",
+       "  'sit': False,\n",
+       "  'class': False,\n",
+       "  'didnt': False,\n",
+       "  'xx': False,\n",
+       "  'real': False,\n",
+       "  'hair': False,\n",
+       "  'aw': False,\n",
+       "  'mine': False,\n",
+       "  'dream': False,\n",
+       "  'repli': False,\n",
+       "  'move': False,\n",
+       "  'chang': False,\n",
+       "  'forward': False,\n",
+       "  'rock': False,\n",
+       "  'month': False,\n",
+       "  'stupid': False,\n",
+       "  'beach': False,\n",
+       "  'open': False,\n",
+       "  'write': False,\n",
+       "  'kinda': False,\n",
+       "  'idea': False,\n",
+       "  'outsid': False,\n",
+       "  'tinyurl': False,\n",
+       "  'brother': False,\n",
+       "  'almost': False,\n",
+       "  'came': False,\n",
+       "  'wake': False,\n",
+       "  'seen': False,\n",
+       "  'word': False,\n",
+       "  'whole': False,\n",
+       "  'dog': False,\n",
+       "  'hang': False,\n",
+       "  'send': False,\n",
+       "  'plurk': False,\n",
+       "  'everyth': False,\n",
+       "  'person': False,\n",
+       "  'list': False,\n",
+       "  'part': False,\n",
+       "  'everi': False,\n",
+       "  'probabl': False,\n",
+       "  'anyon': False,\n",
+       "  'onc': False,\n",
+       "  'comput': False,\n",
+       "  'goodnight': False,\n",
+       "  'quit': False,\n",
+       "  'fan': False,\n",
+       "  'super': False,\n",
+       "  'full': False,\n",
+       "  'minut': False,\n",
+       "  'offic': False,\n",
+       "  'danc': False,\n",
+       "  'trip': False,\n",
+       "  'awww': False,\n",
+       "  'true': False,\n",
+       "  'pick': False,\n",
+       "  'far': False,\n",
+       "  'pain': False,\n",
+       "  'room': False,\n",
+       "  'facebook': False,\n",
+       "  'favorit': False,\n",
+       "  'coffe': False,\n",
+       "  'news': False,\n",
+       "  'took': False,\n",
+       "  'headach': False,\n",
+       "  'blog': False,\n",
+       "  'pas': False,\n",
+       "  'care': False,\n",
+       "  'site': False,\n",
+       "  'fail': False,\n",
+       "  'mother': False,\n",
+       "  'hello': False,\n",
+       "  'boo': False,\n",
+       "  'forgot': False,\n",
+       "  'dude': False,\n",
+       "  'bring': False,\n",
+       "  'el': False,\n",
+       "  'vote': True,\n",
+       "  'la': False,\n",
+       "  'hit': False,\n",
+       "  'hug': False,\n",
+       "  'learn': False,\n",
+       "  'heard': False,\n",
+       "  'lucki': False,\n",
+       "  'pm': False,\n",
+       "  'crazi': False,\n",
+       "  'anymor': False,\n",
+       "  'fall': False,\n",
+       "  'ticket': False,\n",
+       "  'ago': False,\n",
+       "  'concert': False,\n",
+       "  'st': False,\n",
+       "  'test': False,\n",
+       "  'hehe': False,\n",
+       "  'break': False,\n",
+       "  'sister': False,\n",
+       "  'abl': False,\n",
+       "  'text': False,\n",
+       "  'worri': False,\n",
+       "  'sooo': False,\n",
+       "  'serious': False,\n",
+       "  'lil': False,\n",
+       "  'anyway': False,\n",
+       "  'suppos': False,\n",
+       "  'asleep': False,\n",
+       "  'alon': False,\n",
+       "  'shower': False,\n",
+       "  'spend': False,\n",
+       "  'rest': False,\n",
+       "  'either': False,\n",
+       "  'pay': False,\n",
+       "  'interest': False,\n",
+       "  'awak': False,\n",
+       "  'wear': False,\n",
+       "  'cuz': False,\n",
+       "  'photo': False,\n",
+       "  'visit': False,\n",
+       "  'smile': False,\n",
+       "  'fine': False,\n",
+       "  'mind': False,\n",
+       "  'internet': False,\n",
+       "  'album': False,\n",
+       "  'instead': False,\n",
+       "  'rememb': False,\n",
+       "  'cat': False,\n",
+       "  'catch': False,\n",
+       "  'btw': False,\n",
+       "  'email': False,\n",
+       "  'ah': False,\n",
+       "  'set': False,\n",
+       "  'link': False,\n",
+       "  'onlin': False,\n",
+       "  'red': False,\n",
+       "  'add': False,\n",
+       "  'kind': False,\n",
+       "  'mileycyru': False,\n",
+       "  'train': False,\n",
+       "  'hell': False,\n",
+       "  'sign': False,\n",
+       "  'agre': False,\n",
+       "  'half': False,\n",
+       "  'close': False,\n",
+       "  'sunni': False,\n",
+       "  'bout': False,\n",
+       "  'june': False,\n",
+       "  'ice': False,\n",
+       "  'fix': False,\n",
+       "  'top': False,\n",
+       "  'ladi': False,\n",
+       "  'min': False,\n",
+       "  'season': False,\n",
+       "  'wont': False,\n",
+       "  'lmao': False,\n",
+       "  'bought': False,\n",
+       "  'hand': False,\n",
+       "  'ride': False,\n",
+       "  'dress': False,\n",
+       "  'annoy': False,\n",
+       "  'decid': False,\n",
+       "  'short': False,\n",
+       "  'blip': False,\n",
+       "  'ipod': False,\n",
+       "  'wed': False,\n",
+       "  'kill': False,\n",
+       "  'cut': False,\n",
+       "  'nap': False,\n",
+       "  'broke': False,\n",
+       "  'co': False,\n",
+       "  'second': False,\n",
+       "  'scare': False,\n",
+       "  'card': False,\n",
+       "  'join': False,\n",
+       "  'cook': False,\n",
+       "  'fm': False,\n",
+       "  'holiday': False,\n",
+       "  'togeth': False,\n",
+       "  'sometim': False,\n",
+       "  'youtub': False,\n",
+       "  'order': False,\n",
+       "  'soo': False,\n",
+       "  'offici': False,\n",
+       "  'weird': False,\n",
+       "  'band': False,\n",
+       "  'nite': False,\n",
+       "  'page': False,\n",
+       "  'high': False,\n",
+       "  'sore': False,\n",
+       "  'problem': False,\n",
+       "  'stori': False,\n",
+       "  'tommcfli': False,\n",
+       "  'save': False,\n",
+       "  'reason': False,\n",
+       "  'nd': False,\n",
+       "  'laptop': False,\n",
+       "  'side': False,\n",
+       "  'graduat': False,\n",
+       "  'yea': False,\n",
+       "  'crap': False,\n",
+       "  'told': False,\n",
+       "  'question': False,\n",
+       "  'goe': False,\n",
+       "  'forget': False,\n",
+       "  'throat': False,\n",
+       "  'congrat': False,\n",
+       "  'homework': False,\n",
+       "  'relax': False,\n",
+       "  'hmm': False,\n",
+       "  'shirt': False,\n",
+       "  'sim': False,\n",
+       "  'bye': False,\n",
+       "  'stuck': False,\n",
+       "  'tuesday': False,\n",
+       "  'til': False,\n",
+       "  'pack': False,\n",
+       "  'figur': False,\n",
+       "  'gym': False,\n",
+       "  'air': False,\n",
+       "  'breakfast': False,\n",
+       "  'broken': False,\n",
+       "  'past': False,\n",
+       "  'ive': False,\n",
+       "  'worst': False,\n",
+       "  'award': False,\n",
+       "  'citi': False,\n",
+       "  'park': False,\n",
+       "  'dear': False,\n",
+       "  'laugh': False,\n",
+       "  'sigh': False,\n",
+       "  'download': False,\n",
+       "  'perfect': False,\n",
+       "  'le': False,\n",
+       "  'definit': False,\n",
+       "  'afternoon': False,\n",
+       "  'celebr': False,\n",
+       "  'mad': False,\n",
+       "  'david': False,\n",
+       "  'sing': False,\n",
+       "  'answer': False,\n",
+       "  'slow': False,\n",
+       "  'differ': False,\n",
+       "  'fast': False,\n",
+       "  'cours': False,\n",
+       "  'hungri': False,\n",
+       "  'church': False,\n",
+       "  'lie': False,\n",
+       "  'fb': False,\n",
+       "  'luv': False,\n",
+       "  'chocol': False,\n",
+       "  'chanc': False,\n",
+       "  'goin': False,\n",
+       "  'record': False,\n",
+       "  'comment': False,\n",
+       "  'travel': False,\n",
+       "  'dm': False,\n",
+       "  'uk': False,\n",
+       "  'moon': False,\n",
+       "  'complet': False,\n",
+       "  'sleepi': False,\n",
+       "  'foot': False,\n",
+       "  'ddlovato': False,\n",
+       "  'mr': False,\n",
+       "  'tea': False,\n",
+       "  'manag': False,\n",
+       "  'fli': False,\n",
+       "  'easi': False,\n",
+       "  'store': False,\n",
+       "  'math': False,\n",
+       "  'depress': False,\n",
+       "  'jealou': False,\n",
+       "  'line': False,\n",
+       "  'cake': False,\n",
+       "  'tour': False,\n",
+       "  'freak': False,\n",
+       "  'dead': False,\n",
+       "  'understand': False,\n",
+       "  'worth': False,\n",
+       "  'team': False,\n",
+       "  'plu': False,\n",
+       "  'box': False,\n",
+       "  'longer': False,\n",
+       "  'account': False,\n",
+       "  'ppl': False,\n",
+       "  'water': False,\n",
+       "  'moment': False,\n",
+       "  'support': False,\n",
+       "  'fell': False,\n",
+       "  'mood': False,\n",
+       "  'sadli': False,\n",
+       "  'messag': False,\n",
+       "  'rather': False,\n",
+       "  'woman': False,\n",
+       "  'black': False,\n",
+       "  'upset': False,\n",
+       "  'yep': False,\n",
+       "  'version': False,\n",
+       "  'three': False,\n",
+       "  'episod': False,\n",
+       "  'radio': False,\n",
+       "  'unfortun': False,\n",
+       "  'except': False,\n",
+       "  'mum': False,\n",
+       "  'cream': False,\n",
+       "  'met': False,\n",
+       "  'chat': False,\n",
+       "  'begin': False,\n",
+       "  'shot': False,\n",
+       "  'mac': False,\n",
+       "  'cousin': False,\n",
+       "  'camp': False,\n",
+       "  'share': False,\n",
+       "  'app': False,\n",
+       "  'load': False,\n",
+       "  'appar': False,\n",
+       "  'shame': False,\n",
+       "  'leg': False,\n",
+       "  'beer': False,\n",
+       "  'jona': False,\n",
+       "  'ad': False,\n",
+       "  'colleg': False,\n",
+       "  'kick': False,\n",
+       "  'count': False,\n",
+       "  'coupl': False,\n",
+       "  'felt': False,\n",
+       "  'point': False,\n",
+       "  'nope': False,\n",
+       "  'bum': False,\n",
+       "  'ach': False,\n",
+       "  'due': False,\n",
+       "  'via': False,\n",
+       "  'stress': False,\n",
+       "  'hr': False,\n",
+       "  'flight': False,\n",
+       "  'father': False,\n",
+       "  'babe': False,\n",
+       "  'hubbi': False,\n",
+       "  'design': False,\n",
+       "  'ate': False,\n",
+       "  'blue': False,\n",
+       "  'bike': False,\n",
+       "  'bitch': False,\n",
+       "  'absolut': False,\n",
+       "  'town': False,\n",
+       "  'upload': False,\n",
+       "  'bless': False,\n",
+       "  'xd': False,\n",
+       "  'burn': False,\n",
+       "  'club': False,\n",
+       "  'lay': False,\n",
+       "  'special': False,\n",
+       "  'flu': False,\n",
+       "  'fantast': False,\n",
+       "  'pool': False,\n",
+       "  'warm': False,\n",
+       "  'bet': False,\n",
+       "  'son': False,\n",
+       "  'lazi': False,\n",
+       "  'soooo': False,\n",
+       "  'ear': False,\n",
+       "  'followfriday': False,\n",
+       "  'power': False,\n",
+       "  'forev': False,\n",
+       "  'smell': False,\n",
+       "  'lone': False,\n",
+       "  'date': False,\n",
+       "  'twit': False,\n",
+       "  'vacat': False,\n",
+       "  'wit': False,\n",
+       "  'john': False,\n",
+       "  'miley': False,\n",
+       "  'swim': False,\n",
+       "  'sort': False,\n",
+       "  'cheer': False,\n",
+       "  'wors': False,\n",
+       "  'hotel': False,\n",
+       "  'bodi': False,\n",
+       "  'crash': False,\n",
+       "  'usual': False,\n",
+       "  'practic': False,\n",
+       "  'wtf': False,\n",
+       "  'knew': False,\n",
+       "  'alright': False,\n",
+       "  'type': False,\n",
+       "  'yo': False,\n",
+       "  'horribl': False,\n",
+       "  'plane': False,\n",
+       "  'road': False,\n",
+       "  'green': False,\n",
+       "  'meant': False,\n",
+       "  'yummi': False,\n",
+       "  'realiz': False,\n",
+       "  'myspac': False,\n",
+       "  'english': False,\n",
+       "  'ju': False,\n",
+       "  'shoot': False,\n",
+       "  'connect': False,\n",
+       "  'websit': False,\n",
+       "  'cover': False,\n",
+       "  'dvd': False,\n",
+       "  'product': False,\n",
+       "  'juli': False,\n",
+       "  'safe': False,\n",
+       "  'ahhh': False,\n",
+       "  'note': False,\n",
+       "  'thx': False,\n",
+       "  'beat': False,\n",
+       "  'pull': False,\n",
+       "  'woo': False,\n",
+       "  'parent': False,\n",
+       "  'sent': False,\n",
+       "  'present': False,\n",
+       "  'art': False,\n",
+       "  'prob': False,\n",
+       "  'cd': False,\n",
+       "  'appl': False,\n",
+       "  'film': False,\n",
+       "  'bbq': False,\n",
+       "  'fact': False,\n",
+       "  'stomach': False,\n",
+       "  'drop': False,\n",
+       "  'chees': False,\n",
+       "  'yup': False,\n",
+       "  'sunshin': False,\n",
+       "  'child': False,\n",
+       "  'tom': False,\n",
+       "  'sat': False,\n",
+       "  'remind': False,\n",
+       "  'histori': False,\n",
+       "  'sold': False,\n",
+       "  'paper': False,\n",
+       "  'bird': False,\n",
+       "  'addict': False,\n",
+       "  'tear': False,\n",
+       "  'doctor': False,\n",
+       "  'doesnt': False,\n",
+       "  'ahh': False,\n",
+       "  'step': False,\n",
+       "  'inspir': False,\n",
+       "  'star': False,\n",
+       "  'fair': False,\n",
+       "  'disappoint': False,\n",
+       "  'terribl': False,\n",
+       "  'mtv': False,\n",
+       "  'da': False,\n",
+       "  'spent': False,\n",
+       "  'white': False,\n",
+       "  'especi': False,\n",
+       "  'pink': False,\n",
+       "  'garden': False,\n",
+       "  'lose': False,\n",
+       "  'interview': False,\n",
+       "  'laker': False,\n",
+       "  'idk': False,\n",
+       "  'shoe': False,\n",
+       "  'allow': False,\n",
+       "  'alot': False,\n",
+       "  'tummi': False,\n",
+       "  'appreci': False,\n",
+       "  'block': False,\n",
+       "  'goodby': False,\n",
+       "  'watchin': False,\n",
+       "  'dark': False,\n",
+       "  'hahah': False,\n",
+       "  'bar': False,\n",
+       "  'door': False,\n",
+       "  'bb': False,\n",
+       "  'gd': False,\n",
+       "  'london': False,\n",
+       "  'cancel': False,\n",
+       "  'project': False,\n",
+       "  'surpris': False,\n",
+       "  'promis': False,\n",
+       "  'stand': False,\n",
+       "  'yr': False,\n",
+       "  'angel': False,\n",
+       "  'cooki': False,\n",
+       "  'chill': False,\n",
+       "  'land': False,\n",
+       "  'dure': False,\n",
+       "  'event': False,\n",
+       "  'havent': False,\n",
+       "  'storm': False,\n",
+       "  'background': False,\n",
+       "  'airport': False,\n",
+       "  'si': False,\n",
+       "  'edit': False,\n",
+       "  'mention': False,\n",
+       "  'number': False,\n",
+       "  'futur': False,\n",
+       "  'becom': False,\n",
+       "  'possibl': False,\n",
+       "  'revis': False,\n",
+       "  'ouch': False,\n",
+       "  'extra': False,\n",
+       "  'slept': False,\n",
+       "  'light': False,\n",
+       "  'window': False,\n",
+       "  'age': False,\n",
+       "  'boyfriend': False,\n",
+       "  'act': False,\n",
+       "  'small': False,\n",
+       "  'bu': False,\n",
+       "  'gave': False,\n",
+       "  'demi': False,\n",
+       "  'current': False,\n",
+       "  'mommi': False,\n",
+       "  'mess': False,\n",
+       "  'uh': False,\n",
+       "  'channel': False,\n",
+       "  'search': False,\n",
+       "  'sale': False,\n",
+       "  'price': False,\n",
+       "  'fri': False,\n",
+       "  'touch': False,\n",
+       "  'ball': False,\n",
+       "  'chicken': False,\n",
+       "  'finger': False,\n",
+       "  'huh': False,\n",
+       "  'fight': False,\n",
+       "  'mail': False,\n",
+       "  'front': False,\n",
+       "  'web': False,\n",
+       "  'voic': False,\n",
+       "  'piss': False,\n",
+       "  'daddi': False,\n",
+       "  'eh': False,\n",
+       "  'chip': False,\n",
+       "  'recommend': False,\n",
+       "  'wine': False,\n",
+       "  'tr': False,\n",
+       "  'puppi': False,\n",
+       "  'street': False,\n",
+       "  'scari': False,\n",
+       "  'arm': False,\n",
+       "  'hmmm': False,\n",
+       "  'expect': False,\n",
+       "  'twilight': False,\n",
+       "  'kitti': False,\n",
+       "  'round': False,\n",
+       "  'bug': False,\n",
+       "  'pizza': False,\n",
+       "  'itun': False,\n",
+       "  'king': False,\n",
+       "  'huge': False,\n",
+       "  'coz': False,\n",
+       "  'color': False,\n",
+       "  'kati': False,\n",
+       "  'confus': False,\n",
+       "  'speak': False,\n",
+       "  'tip': False,\n",
+       "  'xxx': False,\n",
+       "  'jonasbroth': False,\n",
+       "  'bag': False,\n",
+       "  'nick': False,\n",
+       "  'xo': False,\n",
+       "  'ny': False,\n",
+       "  'kate': False,\n",
+       "  'caught': False,\n",
+       "  'hospit': False,\n",
+       "  'deserv': False,\n",
+       "  'gay': False,\n",
+       "  'xoxo': False,\n",
+       "  'nobodi': False,\n",
+       "  'bday': False,\n",
+       "  'wast': False,\n",
+       "  'servic': False,\n",
+       "  'invit': False,\n",
+       "  'id': False,\n",
+       "  'hill': False,\n",
+       "  'offer': False,\n",
+       "  'low': False,\n",
+       "  'along': False,\n",
+       "  'thursday': False,\n",
+       "  'kiss': False,\n",
+       "  'jb': False,\n",
+       "  'exhaust': False,\n",
+       "  'drunk': False,\n",
+       "  'impress': False,\n",
+       "  'rip': False,\n",
+       "  'code': False,\n",
+       "  'paint': False,\n",
+       "  'em': False,\n",
+       "  'matter': False,\n",
+       "  'dunno': False,\n",
+       "  'jon': False,\n",
+       "  'mile': False,\n",
+       "  'wednesday': False,\n",
+       "  'blood': False,\n",
+       "  'ahead': False,\n",
+       "  'googl': False,\n",
+       "  'avail': False,\n",
+       "  'england': False,\n",
+       "  'case': False,\n",
+       "  'exactli': False,\n",
+       "  'hun': False,\n",
+       "  'wash': False,\n",
+       "  'magic': False,\n",
+       "  'delet': False,\n",
+       "  'tast': False,\n",
+       "  'wet': False,\n",
+       "  'notic': False,\n",
+       "  'proud': False,\n",
+       "  'jame': False,\n",
+       "  'fire': False,\n",
+       "  'continu': False,\n",
+       "  'non': False,\n",
+       "  'countri': False,\n",
+       "  'vip': False,\n",
+       "  'taylor': False,\n",
+       "  'earlier': False,\n",
+       "  'chri': False,\n",
+       "  'kno': False,\n",
+       "  'ruin': False,\n",
+       "  'direct': False,\n",
+       "  'arriv': False,\n",
+       "  'heat': False,\n",
+       "  'prepar': False,\n",
+       "  'fit': False,\n",
+       "  'pray': False,\n",
+       "  'doubt': False,\n",
+       "  'hold': False,\n",
+       "  'race': False,\n",
+       "  'everybodi': False,\n",
+       "  'fav': False,\n",
+       "  'daughter': False,\n",
+       "  'track': False,\n",
+       "  'honey': False,\n",
+       "  'troubl': False,\n",
+       "  'taken': False,\n",
+       "  'brain': False,\n",
+       "  'tune': False,\n",
+       "  'consid': False,\n",
+       "  'bro': False,\n",
+       "  'net': False,\n",
+       "  'joe': False,\n",
+       "  'husband': False,\n",
+       "  'sky': False,\n",
+       "  'shut': False,\n",
+       "  'tonit': False,\n",
+       "  'normal': False,\n",
+       "  'gorgeou': False,\n",
+       "  'apart': False,\n",
+       "  'ff': False,\n",
+       "  'choic': False,\n",
+       "  'etc': False,\n",
+       "  'jordanknight': False,\n",
+       "  'somewher': False,\n",
+       "  'behind': False,\n",
+       "  'lesson': False,\n",
+       "  'fill': False,\n",
+       "  'camera': False,\n",
+       "  'nail': False,\n",
+       "  'nah': False,\n",
+       "  'cross': False,\n",
+       "  'couldnt': False,\n",
+       "  'ohh': False,\n",
+       "  'delici': False,\n",
+       "  'instal': False,\n",
+       "  'secret': False,\n",
+       "  'screen': False,\n",
+       "  'marri': False,\n",
+       "  'forc': False,\n",
+       "  'hangov': False,\n",
+       "  'issu': False,\n",
+       "  'paul': False,\n",
+       "  'darn': False,\n",
+       "  'profil': False,\n",
+       "  'group': False,\n",
+       "  'suggest': False,\n",
+       "  'aint': False,\n",
+       "  'memori': False,\n",
+       "  'tweep': False,\n",
+       "  'spam': False,\n",
+       "  'winter': False,\n",
+       "  'smoke': False,\n",
+       "  'sam': False,\n",
+       "  'compani': False,\n",
+       "  'ryan': False,\n",
+       "  'self': False,\n",
+       "  'hannah': False,\n",
+       "  'bath': False,\n",
+       "  'prefer': False,\n",
+       "  'sweeti': False,\n",
+       "  'death': False,\n",
+       "  'blow': False,\n",
+       "  'shall': False,\n",
+       "  'screw': False,\n",
+       "  'often': False,\n",
+       "  'dat': False,\n",
+       "  'system': False,\n",
+       "  'disney': False,\n",
+       "  'state': False,\n",
+       "  'sa': False,\n",
+       "  'result': False,\n",
+       "  'peac': False,\n",
+       "  'bunch': False,\n",
+       "  'creat': False,\n",
+       "  'american': False,\n",
+       "  'sushi': False,\n",
+       "  'adam': False,\n",
+       "  'nyc': False,\n",
+       "  'ooh': False,\n",
+       "  'crappi': False,\n",
+       "  'mo': False,\n",
+       "  'experi': False,\n",
+       "  'dang': False,\n",
+       "  ...},\n",
+       " 0)"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def create_feature_set(document, selected_words):\n",
+    "    document_words = set(document)\n",
+    "    features = {}\n",
+    "    for word in selected_words:\n",
+    "        features[word] = (word in document_words)\n",
+    "    \n",
+    "    return features\n",
+    "feature_set = []\n",
+    "for index, row in data.iterrows():\n",
+    "    document = row['text_processed']\n",
+    "    sentiment = row['target']\n",
+    "    features = create_feature_set(document, selected_words)\n",
+    "    feature_set.append((features, sentiment))\n",
+    "\n",
+    "for features, sentiment in feature_set:\n",
+    "    data = data.sample(10000)\n",
+    "features, sentiment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# From here forward optional "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Building and Traininng Naive Bayes Model\n",
+    "\n",
+    "    In this step you will split your feature set into a training and a test set. Then you will create a Bayes classifier instance using `nltk.NaiveBayesClassifier.train` ([example](https://www.nltk.org/book/ch06.html)) to train with the training dataset.\n",
+    "\n",
+    "After training the model, call `classifier.show_most_informative_features()` to inspect the most important features. The output will look like:\n",
+    "\n",
+    "```\n",
+    "Most Informative Features\n",
+    "\t    snow = True            False : True   =     34.3 : 1.0\n",
+    "\t  easter = True            False : True   =     26.2 : 1.0\n",
+    "\t headach = True            False : True   =     20.9 : 1.0\n",
+    "\t    argh = True            False : True   =     17.6 : 1.0\n",
+    "\tunfortun = True            False : True   =     16.9 : 1.0\n",
+    "\t    jona = True             True : False  =     16.2 : 1.0\n",
+    "\t     ach = True            False : True   =     14.9 : 1.0\n",
+    "\t     sad = True            False : True   =     13.0 : 1.0\n",
+    "\t  parent = True            False : True   =     12.9 : 1.0\n",
+    "\t  spring = True            False : True   =     12.7 : 1.0\n",
+    "```\n",
+    "\n",
+    "The [following video](https://www.youtube.com/watch?v=rISOsUaTrO4) will help you complete this step. The source code in this video can be found [here](https://pythonprogramming.net/naive-bayes-classifier-nltk-tutorial/)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![Building and Training NB](nb-model-building.jpg)](https://www.youtube.com/watch?v=rISOsUaTrO4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Testing Naive Bayes Model\n",
+    "\n",
+    "Now we'll test our classifier with the test dataset. This is done by calling `nltk.classify.accuracy(classifier, test)`.\n",
+    "\n",
+    "As mentioned in one of the tutorial videos, a Naive Bayes model is considered OK if your accuracy score is over 0.6. If your accuracy score is over 0.7, you've done a great job!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bonus Question 1: Improve Model Performance\n",
+    "\n",
+    "If you are still not exhausted so far and want to dig deeper, try to improve your classifier performance. There are many aspects you can dig into, for example:\n",
+    "\n",
+    "* Improve stemming and lemmatization. Inspect your bag of words and the most important features. Are there any words you should furuther remove from analysis? You can append these words to further remove to the stop words list.\n",
+    "\n",
+    "* Remember we only used the top 5,000 features to build model? Try using different numbers of top features. The bottom line is to use as few features as you can without compromising your model performance. The fewer features you select into your model, the faster your model is trained. Then you can use a larger sample size to improve your model accuracy score."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bonus Question 2: Machine Learning Pipeline\n",
+    "\n",
+    "In a new Jupyter Notebook, combine all your codes into a function (or a class). Your new function will execute the complete machine learning pipeline job by receiving the dataset location and output the classifier. This will allow you to use your function to predict the sentiment of any tweet in real time. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bonus Question 3: Apache Spark\n",
+    "\n",
+    "If you have completed the Apache Spark advanced topic lab, what you can do is to migrate your pipeline from local to a Databricks Notebook. Share your notebook with your instructor and classmates to show off your achievements!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# your code here"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/your-code/challenge-2.ipynb b/your-code/challenge-2.ipynb
deleted file mode 100644
index 6b0e116..0000000
--- a/your-code/challenge-2.ipynb
+++ /dev/null
@@ -1,320 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Challenge 2: Sentiment Analysis\n",
-    "\n",
-    "In this challenge we will learn sentiment analysis and practice performing sentiment analysis on Twitter tweets. \n",
-    "\n",
-    "## Introduction\n",
-    "\n",
-    "Sentiment analysis is to *systematically identify, extract, quantify, and study affective states and subjective information* based on texts ([reference](https://en.wikipedia.org/wiki/Sentiment_analysis)). In simple words, it's to understand whether a person is happy or unhappy in producing the piece of text. Why we (or rather, companies) care about sentiment in texts? It's because by understanding the sentiments in texts, we will be able to know if our customers are happy or unhappy about our products and services. If they are unhappy, the subsequent action is to figure out what have caused the unhappiness and make improvements.\n",
-    "\n",
-    "Basic sentiment analysis only understands the *positive* or *negative* (sometimes *neutral* too) polarities of the sentiment. More advanced sentiment analysis will also consider dimensions such as agreement, subjectivity, confidence, irony, and so on. In this challenge we will conduct the basic positive vs negative sentiment analysis based on real Twitter tweets.\n",
-    "\n",
-    "NLTK comes with a [sentiment analysis package](https://www.nltk.org/api/nltk.sentiment.html). This package is great for dummies to perform sentiment analysis because it requires only the textual data to make predictions. For example:\n",
-    "\n",
-    "```python\n",
-    ">>> from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
-    ">>> txt = \"Ironhack is a Global Tech School ranked num 2 worldwide. 
",
-    "
",
-    "Our mission is to help people transform their careers and join a thriving community of tech professionals that love what they do.\"\n",
-    ">>> analyzer = SentimentIntensityAnalyzer()\n",
-    ">>> analyzer.polarity_scores(txt)\n",
-    "{'neg': 0.0, 'neu': 0.741, 'pos': 0.259, 'compound': 0.8442}\n",
-    "```\n",
-    "\n",
-    "In this challenge, however, you will not use NLTK's sentiment analysis package because in your Machine Learning training in the past 2 weeks you have learned how to make predictions more accurate than that. The [tweets data](https://www.kaggle.com/kazanova/sentiment140) we will be using today are already coded for the positive/negative sentiment. You will be able to use the Naïve Bayes classifier you learned in the lesson to predict the sentiment of tweets based on the labels."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Conducting Sentiment Analysis\n",
-    "\n",
-    "### Loading and Exploring Data\n",
-    "\n",
-    "The dataset we'll be using today is located on Kaggle (https://www.kaggle.com/kazanova/sentiment140). Once you have downloaded and imported the dataset, it you will need to define the columns names: df.columns = ['target','id','date','flag','user','text']\n",
-    "\n",
-    "*Notes:* \n",
-    "\n",
-    "* The dataset is huuuuge (1.6m tweets). When you develop your data analysis codes, you can sample a subset of the data (e.g. 20k records) so that you will save a lot of time when you test your codes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Prepare Textual Data for Sentiment Analysis\n",
-    "\n",
-    "Now, apply the functions you have written in Challenge 1 to your whole data set. These functions include:\n",
-    "\n",
-    "* `clean_up()`\n",
-    "\n",
-    "* `tokenize()`\n",
-    "\n",
-    "* `stem_and_lemmatize()`\n",
-    "\n",
-    "* `remove_stopwords()`\n",
-    "\n",
-    "Create a new column called `text_processed` in the dataframe to contain the processed data. At the end, your `text_processed` column should contain lists of word tokens that are cleaned up. Your data should look like below:\n",
-    "\n",
-    "![Processed Data](data-cleaning-results.png)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Creating Bag of Words\n",
-    "\n",
-    "The purpose of this step is to create a [bag of words](https://en.wikipedia.org/wiki/Bag-of-words_model) from the processed data. The bag of words contains all the unique words in your whole text body (a.k.a. *corpus*) with the number of occurrence of each word. It will allow you to understand which words are the most important features across the whole corpus.\n",
-    "\n",
-    "Also, you can imagine you will have a massive set of words. The less important words (i.e. those of very low number of occurrence) do not contribute much to the sentiment. Therefore, you only need to use the most important words to build your feature set in the next step. In our case, we will use the top 5,000 words with the highest frequency to build the features.\n",
-    "\n",
-    "In the cell below, combine all the words in `text_processed` and calculate the frequency distribution of all words. A convenient library to calculate the term frequency distribution is NLTK's `FreqDist` class ([documentation](https://www.nltk.org/api/nltk.html#module-nltk.probability)). Then select the top 5,000 words from the frequency distribution."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Building Features\n",
-    "\n",
-    "Now let's build the features. Using the top 5,000 words, create a 2-dimensional matrix to record whether each of those words is contained in each document (tweet). Then you also have an output column to indicate whether the sentiment in each tweet is positive. For example, assuming your bag of words has 5 items (`['one', 'two', 'three', 'four', 'five']`) out of 4 documents (`['A', 'B', 'C', 'D']`), your feature set is essentially:\n",
-    "\n",
-    "| Doc | one | two | three | four | five | is_positive |\n",
-    "|---|---|---|---|---|---|---|\n",
-    "| A | True | False | False | True | False | True |\n",
-    "| B | False | False | False | True | True | False |\n",
-    "| C | False | True | False | False | False | True |\n",
-    "| D | True | False | False | False | True | False|\n",
-    "\n",
-    "However, because the `nltk.NaiveBayesClassifier.train` class we will use in the next step does not work with Pandas dataframe, the structure of your feature set should be converted to the Python list looking like below:\n",
-    "\n",
-    "```python\n",
-    "[\n",
-    "\t({\n",
-    "\t\t'one': True,\n",
-    "\t\t'two': False,\n",
-    "\t\t'three': False,\n",
-    "\t\t'four': True,\n",
-    "\t\t'five': False\n",
-    "\t}, True),\n",
-    "\t({\n",
-    "\t\t'one': False,\n",
-    "\t\t'two': False,\n",
-    "\t\t'three': False,\n",
-    "\t\t'four': True,\n",
-    "\t\t'five': True\n",
-    "\t}, False),\n",
-    "\t({\n",
-    "\t\t'one': False,\n",
-    "\t\t'two': True,\n",
-    "\t\t'three': False,\n",
-    "\t\t'four': False,\n",
-    "\t\t'five': False\n",
-    "\t}, True),\n",
-    "\t({\n",
-    "\t\t'one': True,\n",
-    "\t\t'two': False,\n",
-    "\t\t'three': False,\n",
-    "\t\t'four': False,\n",
-    "\t\t'five': True\n",
-    "\t}, False)\n",
-    "]\n",
-    "```\n",
-    "\n",
-    "To help you in this step, watch the [following video](https://www.youtube.com/watch?v=-vVskDsHcVc) to learn how to build the feature set with Python and NLTK. The source code in this video can be found [here](https://pythonprogramming.net/words-as-features-nltk-tutorial/)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "[![Building Features](building-features.jpg)](https://www.youtube.com/watch?v=-vVskDsHcVc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Building and Traininng Naive Bayes Model\n",
-    "\n",
-    "In this step you will split your feature set into a training and a test set. Then you will create a Bayes classifier instance using `nltk.NaiveBayesClassifier.train` ([example](https://www.nltk.org/book/ch06.html)) to train with the training dataset.\n",
-    "\n",
-    "After training the model, call `classifier.show_most_informative_features()` to inspect the most important features. The output will look like:\n",
-    "\n",
-    "```\n",
-    "Most Informative Features\n",
-    "\t    snow = True            False : True   =     34.3 : 1.0\n",
-    "\t  easter = True            False : True   =     26.2 : 1.0\n",
-    "\t headach = True            False : True   =     20.9 : 1.0\n",
-    "\t    argh = True            False : True   =     17.6 : 1.0\n",
-    "\tunfortun = True            False : True   =     16.9 : 1.0\n",
-    "\t    jona = True             True : False  =     16.2 : 1.0\n",
-    "\t     ach = True            False : True   =     14.9 : 1.0\n",
-    "\t     sad = True            False : True   =     13.0 : 1.0\n",
-    "\t  parent = True            False : True   =     12.9 : 1.0\n",
-    "\t  spring = True            False : True   =     12.7 : 1.0\n",
-    "```\n",
-    "\n",
-    "The [following video](https://www.youtube.com/watch?v=rISOsUaTrO4) will help you complete this step. The source code in this video can be found [here](https://pythonprogramming.net/naive-bayes-classifier-nltk-tutorial/)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "[![Building and Training NB](nb-model-building.jpg)](https://www.youtube.com/watch?v=rISOsUaTrO4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Testing Naive Bayes Model\n",
-    "\n",
-    "Now we'll test our classifier with the test dataset. This is done by calling `nltk.classify.accuracy(classifier, test)`.\n",
-    "\n",
-    "As mentioned in one of the tutorial videos, a Naive Bayes model is considered OK if your accuracy score is over 0.6. If your accuracy score is over 0.7, you've done a great job!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Bonus Question 1: Improve Model Performance\n",
-    "\n",
-    "If you are still not exhausted so far and want to dig deeper, try to improve your classifier performance. There are many aspects you can dig into, for example:\n",
-    "\n",
-    "* Improve stemming and lemmatization. Inspect your bag of words and the most important features. Are there any words you should furuther remove from analysis? You can append these words to further remove to the stop words list.\n",
-    "\n",
-    "* Remember we only used the top 5,000 features to build model? Try using different numbers of top features. The bottom line is to use as few features as you can without compromising your model performance. The fewer features you select into your model, the faster your model is trained. Then you can use a larger sample size to improve your model accuracy score."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Bonus Question 2: Machine Learning Pipeline\n",
-    "\n",
-    "In a new Jupyter Notebook, combine all your codes into a function (or a class). Your new function will execute the complete machine learning pipeline job by receiving the dataset location and output the classifier. This will allow you to use your function to predict the sentiment of any tweet in real time. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Bonus Question 3: Apache Spark\n",
-    "\n",
-    "If you have completed the Apache Spark advanced topic lab, what you can do is to migrate your pipeline from local to a Databricks Notebook. Share your notebook with your instructor and classmates to show off your achievements!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}