How does sentiment analysis work?

Simple Chat runs AI analysis on customer messages and assigns each one a sentiment score on a 1-5 scale, where lower is more negative and higher is more positive. The same analysis tags topics, product mentions, and broader conversation themes. All of it rolls up into Reports, especially the Sentiment tab, so you can spot patterns instead of reading every thread.

Only analyzed messages with valid sentiment scores feed the aggregate sentiment reporting. That's what keeps the charts consistent.

Reading the Sentiment report

The report breaks messages down by sentiment level, often with the products and topics most associated with each band. Two questions it's good at answering: which products correlate with very negative (1-2) messages, and whether a topic improved after you updated the FAQ or the shipping policy. The same date ranges and customer filters as other reports apply, so you can compare segments, say returning customers against anonymous visitors.

From scores to fixes

1. Review a 30-day Sentiment report after major launches or policy changes.
2. Note products or topics with clusters of low scores.
3. Open sample conversations from the report drill-down and read the exact wording.
4. Fix the root cause (product quality, page clarity, shipping expectations) and recheck the next period.

Sentiment analysis doesn't replace reading critical tickets. It scales pattern detection, so product and support leads know which fixes to prioritize and have evidence when they do.