Naive Bayes for Multilingual Text Classification

Overview

This project implements the Naive Bayes algorithm for text classification across multiple languages. The aim is to explore how Naive Bayes, a probabilistic machine learning algorithm, can be applied effectively to multilingual text data in various natural language processing (NLP) tasks.

Features

• Naive Bayes Implementation: Custom implementation of the Naive Bayes algorithm for text classification.
• Multilingual Support: Handles text data from multiple languages.
• Model Evaluation: Evaluates model performance using accuracy, precision, recall, and F1-score.
• Dataset Support: Supports various datasets for multilingual text classification.

Usage

1. Prepare your dataset:

   • Ensure that your dataset is in a suitable format (e.g., CSV or JSON) and contains labeled text data from multiple languages.

2. Train the model:

   • Run the training script to train the Naive Bayes classifier on your dataset.

     python train.py --dataset path_to_your_dataset

3. Evaluate the model:

   • Evaluate the trained model on a test set and view the results.

     python evaluate.py --dataset path_to_your_test_dataset

Datasets

Some example datasets you can use:

• Multilingual Sentiment Analysis Dataset
• Language Identification Dataset
• Spam/Ham Classification Dataset (in multiple languages)