Customer Review Analysis Service for Retail Storefronts

Added Jun 2025 3 design docs

Independent retailers get told exactly what is wrong with their business every day, in their reviews, but few have the time to read hundreds of them or the tools to see the patterns. Was the recent dip in ratings about staff, stock, or the new checkout layout? Answering that today requires reading everything; store owners need a service that reads it for them and reports what matters. The intern builds this analyzer as a Python web service using Flask. The application accepts customer feedback through submission endpoints or batch upload, stores it, and runs each entry through OpenAI models to classify sentiment and identify the aspect of the business being discussed, such as service, pricing, product quality, or store experience. Aggregation endpoints then answer the owner's real questions: overall sentiment over time, the most common complaints this month, and representative quotes for each theme. The intern designs the data model, the classification prompts, and the summary responses that turn raw reviews into a Monday-morning briefing. The project gives an early-stage developer a complete, honest introduction to applied NLP in commerce: building a Flask API, integrating an LLM for classification rather than chat, structuring model output for aggregation, and presenting analysis a non-technical business owner can act on.

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