Social Media Sentiment Analysis for Local Businesses

This study develops a unified Yelp-based sentiment analysis framework that systematically compares lexicon-based, traditional machine-learning, and transformer-based models under a common evaluation setting. The best-performing model, DistilBERT, is then applied to a separate 300,000-review sample to support category-, aspect-, and business-level analysis. The main contribution is the integration of sentiment classification with keyword-based aspect detection and category-relative benchmarking to identify interpretable business strengths, weaknesses, and actionable customer-feedback patterns.