How does sentiment analysis work?

Simple Chat analyzes customer messages with AI to classify how the shopper feels, what they are talking about, and which products or themes show up in the conversation. That analysis rolls up into Reports—especially the Sentiment tab—so you can spot trends instead of reading every thread manually.

What gets analyzed on each message

For customer messages, the AI assigns:

• Sentiment on a 1–5 scale, where lower scores reflect more negative emotion and higher scores reflect more positive tone.
• Topics — recurring support themes that aggregate across conversations.
• Product mentions — signals when specific catalog items drive questions or complaints.
• Conversation themes — broader patterns that help you group similar issues.

Only analyzed messages with valid sentiment scores feed aggregate sentiment reporting, which keeps charts consistent.

How sentiment appears in Reports

On the Sentiment report you can see breakdowns by sentiment level, often alongside the products and topics most associated with each band. That layout helps answer questions like:

• Which products correlate with very negative (1–2) messages?
• Are certain topics improving after we updated the FAQ or shipping policy?

Use the same date ranges and customer filters as other reports when you want to compare segments—for example, returning customers vs anonymous visitors.

Turning sentiment data into action

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 to see exact wording.
4. Fix root causes (product quality, page clarity, shipping expectations) and recheck the next period.

Sentiment analysis does not replace reading critical tickets, but it scales pattern detection so product and support leads can prioritize fixes with evidence.