E-Commerce Review Mining and Sentiment Analytics Platform

Added Jun 2025 3 design docs

A mid-sized online retailer can accumulate hundreds of thousands of product reviews, and buried in them is precise intelligence: which product flaws drive returns, which features customers mention before repurchasing, and how sentiment shifts after a packaging change. Manual reading cannot surface this at scale, and keyword tools miss nuance, so merchandising and product teams routinely make decisions blind to what customers have already told them. The intern builds a full analytics platform that mines this review corpus. The backend is a Python Flask application, with LangChain providing the processing machinery: advanced retrieval-augmented generation over the indexed review base so analysts can ask free-form questions grounded in actual reviews, chained extraction pipelines that pull structured fields such as mentioned features, complaints, and comparisons out of unstructured text, and output parsers that force OpenAI model responses into clean schemas ready for aggregation. On top of the pipeline the intern builds analytics endpoints covering sentiment trends, theme frequency, and product-level comparisons. As a hard-difficulty project, it demonstrates production-grade generative AI engineering: high-volume text processing, reliable structured extraction, RAG design over noisy real-world data, and the delivery of genuine business analytics from raw customer language.

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