mohamedabdely/Sentiment-Analysis-News-Classification

This project consists of studying the Tunisian population between 2015-2024 based on news articles. Two main models were implemented, with the synergy of dynamic web scraping for data collection, a sentiment analysis model and a topic classification model to get further insights. # SA-NLP: Sentiment Analysis & Topic Classification (2015-2024) This project analyzes population trends and public sentiment based on news articles published between 2015 and 2024. It combines dynamic web scraping with advanced NLP models. ## 📁 Project Structure - **/Scraping**: Python scripts for data collection. - **/Notebook**: Jupyter/Colab notebooks for data processing and model training. - **/Data**: Contains the Lexicon and the News NLP dataset (~28MB). ## 🛠 Setup & Requirements ### 1. Database Configuration (MySQL) The scraping scripts are designed to store data in a MySQL database. To run them: 1. Ensure you have MySQL installed and running. 2. Create a database (e.g., `news_db`). 3. Update the connection strings in the scraping scripts with your local `host`, `user`, and `password`. ### 2. Environment Install the necessary Python libraries in the file REQUIREMENTS.txt