Sentiment Analytics - Streaming Intelligence
Large-scale sentiment analysis system processing 50k+ Amazon reviews with real-time insights and predictive analytics for retail intelligence.
Technologies Used
About This Project
Sentiment Analytics is a comprehensive streaming retail intelligence platform that processes over 50,000 Amazon product reviews to extract actionable business insights. The system employs a robust PySpark ETL pipeline to ingest, process, and analyze review data in real-time. Advanced sentiment analysis algorithms correlate customer emotions with product ratings, identifying trends and patterns that drive purchasing decisions.
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Challenges & Solutions
Processing large volumes of unstructured review data while maintaining real-time performance was challenging. Ensuring accuracy in sentiment analysis across diverse product categories and customer demographics required sophisticated NLP techniques.
Implemented distributed processing with PySpark to handle large data volumes efficiently. Used advanced text preprocessing and sentiment analysis models to improve accuracy across different domains.
Key Learnings
Mastered large-scale data processing techniques and gained experience with cloud-native data architectures. Understanding the importance of data quality and preprocessing in achieving accurate analytical results.