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Review Intelligence with Sentiment + Market Basket Analysis\n\n# Notebook\tDescription\n01_reddit_streaming.ipynb\tUses Reddit API (PRAW) to collect live comments from targeted subreddits.\n02_cleaning_and_sentiment.ipynb\tCleans Reddit data and performs sentiment analysis with TextBlob.\n03_amazon_reviews_analysis.ipynb\tAnalyzes review texts for sentiment and links them with star ratings.\n04_market_basket_model.ipynb\tUses FP-Growth in PySpark for item association rule mining.\n\n# Tech Stack\nLanguage: Python 3.10+\n\nNLP: TextBlob, NLTK\n\nBig Data: PySpark\n\nAPI \u0026 Web: PRAW (Reddit API)\n\nAnalysis: Pandas, NumPy, Matplotlib, Seaborn\n\nModeling: scikit-learn, FP-Growth\n\n# Setup Instructions\n# Install Requirements\nbash\nCopy\nEdit\npip install -r requirements.txt\n# Reddit API Setup\nGo to https://www.reddit.com/prefs/apps\n\nCreate a new app and note your:\n\nclient_id\n\nclient_secret\n\nuser_agent\n\nAdd them to a .env file:\n\nenv\nCopy\nEdit\nREDDIT_CLIENT_ID=your_id\nREDDIT_CLIENT_SECRET=your_secret\nREDDIT_USER_AGENT=shoplytics_pipeline\n# Sample Insights\nPositive Reddit sentiment increases on weekends across eCommerce subreddits.\n\nAmazon product reviews with higher sentiment often correlate with 4- and 5-star ratings.\n\nCommon itemsets like ['phone_case', 'screen_protector'] have high lift and support, ideal for product bundling.\n\n# Future Enhancements\nIntegrate Streamlit dashboard for live Reddit sentiment visualization.\n\nUse BERT or DistilBERT for advanced sentiment classification.\n\nAlert system for detecting sentiment dips across product discussions.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fiamdevnd%2Fsentiment-analytics","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fiamdevnd%2Fsentiment-analytics","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fiamdevnd%2Fsentiment-analytics/lists"}