Text Categorization Comparison

This project explores the necessary steps involved in Text Categorization, and performs a comparative evaluation on a variety of Vectorization and Prediction techniques from Sklearn.

Details

The major steps of Text Categorization are:

• Preprocessing raw text into vectors of various sizes (500, 1000, 2000)
• Training and Testing models
• Evaluation of Models

Vectorization techniques:

• Bag of Words (Sklearn's CountVectorizer)
• TFIDF (Sklearn's TfidfVectorizer)

Models:

• Sklearn's Logistic Regression Classifier
• Sklearn's Linear Support Vector Classifier
• Sklearn's Passive Agressive Classifier
• Sklearn's SGD Classifier using Elasticnet Penalty
• Sklearn's Random Forest Classifier
• Sklearn's Perceptron
• Sklearn's K-Nearest Neighbors with 10 neighbors
• Sklearn's Multi-Layer Perceptron Classifier

Evaluation Statistics:

• Accuracy
• Precision (micro & macro)
• Recall (micro & macro)
• F1 (micro & macro)

Installation

1. Use the package, dependency and environment management system conda to install all dependencies.

conda create -f environment.yml
conda activate tcc_env

2. Download the necessary corpuses

import nltk
nltk.download('reuters')
nltk.download('stopwords')

Usage

python run.py 

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

MIT