Sentiment Analysis
Sentiment analysis is the automatic classification of review text as positive, negative, or neutral, often paired with theme extraction that groups recurring topics like shipping, sizing, or support so a store can read the mood of hundreds of reviews without reading each one.
For a store with thousands of reviews, sentiment analysis turns an unreadable pile into a summary: what share of customers are happy, which products draw complaints, and which themes (fit, delivery, quality, value) keep surfacing. It is most useful as triage. It points you at the products and topics worth a closer human look, rather than replacing that look.
Where it misreads is nuance. Sarcasm ("great, another broken zipper") often scores as positive on the word "great." Mixed reviews that praise the product but criticise shipping get flattened into a single label that loses both points. Negation, slang, and product-specific language trip up general models, so a five-star review and its text sometimes disagree. Treat the scores as a signal, not a verdict, and sample the raw text behind any theme before acting on it.
The deeper value of sentiment is making a review corpus legible: once the themes and the balance of opinion are clear, the same readability is what lets search engines and AI assistants quote your reviews when shoppers ask. Getting existing reviews readable, corroborated, and cited is the gap BetterReviews closes.