Multi-Source Customer Feedback Sentiment Dashboard

Retail and media businesses receive feedback everywhere at once: app reviews, survey responses, support tickets, and social comments. Individually each message is easy to read; collectively they form a stream no team can keep up with, so recurring complaints go unnoticed until they become churn. What managers need is a single view that says what customers are feeling, about which features, and how that is trending.

The intern builds an analysis agent that produces exactly that view. Written in Python, the system ingests feedback from multiple sources into a common format, then uses LangChain pipelines with OpenAI models to classify sentiment, extract the topics and product areas each comment addresses, and generate rolling summaries of what changed since the last period. The results surface in Streamlit dashboards that the intern tailors to different user profiles: an executive sees trend lines and headline issues, while a product owner sees granular topic clusters with representative quotes. The intern designs the ingestion normalizer, the classification prompts, and the profile-aware dashboard views.

The project demonstrates end-to-end applied NLP: sentiment analysis and summarization with LLMs, pipeline construction in LangChain, and the data-storytelling skill of turning thousands of raw comments into dashboards that drive decisions.