ba5liel/Sentiment_Analysis_On_Ethiopian_News

Semantic analysis on Ethiopian news # Sentiment-Analysis-On-Ethiopian-News This project aims to train a machine learning model to categorize news articles, leveraging TensorFlow, Keras, and the Google Translate API. The model scrapes, translates, and classifies news articles into categories based on their sentiment. ## Project Structure - **`sentiment_classification.py`**: Defines the `SentimentModel`, `SentimentTrainer`, and `SentimentPredictor` classes for building, training, and using the sentiment classification model. - **`news_scraper_translator.py`**: Includes `NewsScraper` and `TextTranslator` classes for extracting and translating article content. - **`demo.py`**: Script for training the model and demonstrating its use on news articles. ## Usage To use this project, install the necessary libraries with `pip install -r requirements.txt`, then run `demo.py` to train the model and classify a sample news article. ## Limitations and Future Enhancements ### Current Limitations - **Domain-Specific Training**: The model may not perform well outside the specific news domains it was trained on, due to differences in language usage and topics. - **Language Limitations**: Currently, the translation and classification are optimized for English. Performance may degrade with different languages or nuanced linguistic features. - **Scalability**: The model's performance and speed might decrease as the size and frequency of the data increase. ### Future Enhancements 1. **Advanced Model Techniques**: Implement state-of-the-art models like BERT or RoBERTa for better context understanding and sentiment analysis. 2. **Multi-Language Support**: Enhance the `TextTranslator` class to handle multiple languages more robustly, possibly using advanced detection and translation models. 3. **Dynamic Scraping Capabilities**: Improve the `NewsScraper` class to adapt to various website layouts and content formats dynamically. 4. **Real-Time Classification**: Develop a system to monitor and classify news articles in real- …