/Naive-Bayes-on-Multi-Language-Text

Implementation of Naive Bayes for text classification across multiple languages, focusing on natural language processing and multilingual text analysis.

Primary LanguagePython

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)