Tweetalyze

A sentiment analysis project that uses Twitter data to assess public sentiments through Natural Language Processing (NLP). It features text cleaning, feature extraction, and data visualization, employing a Random Forest classifier to accurately classify tweet sentiments. Gain insights into Twitter trends with this comprehensive analysis tool.

Table of Contents

• Installation
• Usage
• Features
• Data Visualization
• Data Cleaning
• Model Building
• Contributing
• License

Installation

To get started with Tweetalyze, clone the repository and install the required dependencies:

git clone https://github.com/aryapatel10/Tweetalyze.git
cd Tweetalyze
pip install -r requirements.txt
python -m spacy download en_core_web_sm
pip install beautifulsoup4 textblob wordcloud
pip install git+https://github.com/laxmimerit/preprocess_kgptalkie.git --upgrade --force-reinstall

Usage

To run the project, execute the following command in your terminal:

python app.py

Features

• Data Loading: Loads Twitter sentiment data for analysis.
• Feature Extraction: Extracts basic features from tweet text.
• Data Visualization: Creates histograms, pie charts, and word clouds to visualize sentiment distribution.
• Data Cleaning: Cleans tweet text by removing URLs, HTML tags, special characters, and retweets.
• Model Building: Builds a Random Forest classifier for sentiment analysis.
• Model Evaluation: Evaluates model performance using classification metrics.

Data Visualization

The project includes several visualizations, such as:

• Histograms for numerical features
• Pie charts to show sentiment distribution
• Word clouds representing the most common words in each sentiment category

Data Cleaning

The text data is preprocessed to ensure accurate sentiment analysis by:

• Converting text to lowercase Removing URLs, HTML tags, special characters, and retweets

Model Building

The project uses a Random Forest classifier to perform sentiment analysis on the cleaned text data. The model is evaluated using a classification report that includes precision, recall, and F1-score.

Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue for any improvements or suggestions.

LICENSE

This project is licensed under the MIT LICENSE - see the LICENSE file for details.