Mosapmohamd/Sentiment-Analysis-for-TripAdvisor-and-Quora-Egypt

# Tourism Sentiment Analysis and Recommendation System ## Project Overview This project leverages AI and data science to enhance Egypt's tourism sector by analyzing visitor sentiments from online reviews. Using Natural Language Processing (NLP) and Machine Learning (ML), the system classifies sentiments and generates actionable recommendations to improve tourist experiences. bilstmpy-m6s8ts8adzgn4zfzyi… ## Table of Contents - Features - Project Scope - Methodology - Model Architecture - Results - System Components - Usage - Future Work - Team ## Features - **Sentiment Classification**: Advanced ML models to categorize reviews as positive or negative - **Aspect-Based Sentiment Analysis**: Extracting specific aspects tourists are happy or unhappy about - **Recommendation Generation**: AI-powered suggestions for tourism improvement based on negative feedback - **Interactive Web Interface**: Real-time sentiment prediction and visualization through Streamlit - **Data-Driven Insights**: Actionable intelligence for tourism stakeholders ## Project Scope The project analyzes English-language reviews from Tripadvisor and Quora covering Egypt's historical sites, museums, and cultural venues. It includes: - Data collection from online platforms - Text preprocessing and cleaning - Sentiment classification using various ML models - Aspect-based sentiment extraction - LLM-based recommendation generation - Web application deployment for real-time predictions ## Methodology ### Data Collection and Preprocessing 1. **Data Gathering**: Scraped over 30,000 tourism reviews from Tripadvisor and Quora 2. **Text Preprocessing**: - Lowercasing - URL removal - Emoji conversion - Slang replacement - Tokenization - Stopword removal - Lemmatization 3. **Sentiment Annotation**: Employed ensemble of pre-trained models (Roberta, VADER, DistilBERT) with majority voting ### Handling Class Imbalance The dataset contained significantly more positive reviews (34,00 …