Multilingual Sentiment Analysis

This project focuses on performing sentiment analysis on multilingual text data using various machine learning (ML) and deep learning techniques, including Long Short-Term Memory (LSTM) networks and Bidirectional Encoder Representations from Transformers (BERT). The goal is to develop models capable of understanding and classifying sentiments expressed in different languages accurately.

Overview

Sentiment analysis is a natural language processing (NLP) task that involves determining the sentiment expressed in a piece of text, such as positive, negative, or neutral. Multilingual sentiment analysis extends this task to handle text data in multiple languages, which presents unique challenges due to linguistic variations and cultural differences.

Features

• Multilingual Dataset: Utilizes a diverse dataset containing text samples in multiple languages, ensuring robustness and generalization of the models.
• Machine Learning Models: Implements traditional machine learning algorithms such as Support Vector Machines (SVM), Random Forest, and Naive Bayes for sentiment classification.
• LSTM Model: Employs Long Short-Term Memory networks, a type of recurrent neural network (RNN), to capture sequential information and long-term dependencies in the text data.
• BERT Model: Integrates Bidirectional Encoder Representations from Transformers, a state-of-the-art transformer-based model, for contextualized word embeddings and fine-grained sentiment analysis.

Usage

1. Data Preprocessing: Clean and preprocess the text data, including tasks such as tokenization, stemming, and removing stopwords.
2. Model Training: Train the ML models, LSTM, and BERT model on the preprocessed data using appropriate training techniques and hyperparameter tuning.
3. Evaluation: Evaluate the performance of each model using metrics such as accuracy, precision, recall, and F1-score on a held-out test set.
4. Inference: Deploy the trained models to perform sentiment analysis on new multilingual text data and interpret the results.

Requirements

• Python 3.10
• TensorFlow
• PyTorch
• Scikit-learn
• Hugging Face Transformers
• Pandas
• NumPy

Contributing

Contributions to this project are welcome! If you have ideas for improvements or new features, feel free to open an issue or submit a pull request.