Single Label Multi Class Text Classification

The script consists of two parts

• Part A: Sentiment Polarity Classification (5 distinct labels)
• Part B: Subreddit Classification (20 distinct labels)

Both parts involve the following steps:

1. Data pre-processing

Tokenization
Normalization

2. Vectorization

One Hot Encoding
TF-IDF

3. Model Creation

Logistic Regression
SVC
Random Forest
BernoulliNB
Decision Tree

4. Parameter Tuning

GridSearchCV

5. Error Analysis
6. Word Embeddings, optimizing the models created

Word2Vec