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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)