Sentiment Analysis
A rising or falling score tells you something changed; it rarely tells you how customers feel about it. Sentiment Analysis fills that gap by unveiling the tone hidden in your open-text questions — scoring every free-text answer on a positive–neutral–negative scale so you can see the mood behind the metrics and, crucially, read the exact comments driving each side. For CX and Voice-of-Customer teams, this is the early-warning report: when the tone of open text starts trending down, you'll see it here before it shows up in churn, and you can pull up the raw negative verbatims behind any dip in seconds.
It works across multilingual comments, so one view covers every market you survey.
What you'll see
A set of comments overview scorecards opens the report with KPIs your organization can tailor:
- Comment rate %, responses with a comment, and sentiment-analysed responses — how much open text you're collecting and analysing.
- An overall sentiment level — a 1–5 gauge with a plain-language verbal label.
- The positive / neutral / negative split — the share of comments in each band.
From there, the charts move from the big picture down to individual comments:
- A comments combo chart — bars for responses and comments over time, with a comment-rate-% line overlaid, so you see volume and participation together.
- A sentiment gauge — the overall tone at a glance.
- A sentiment-segments donut and table — the positive / neutral / negative breakdown in numbers.
- A "Comments & Sentiment" verbatim table — every comment with its assigned sentiment, so you can read the real words behind a band.
- A sentiment timeline — monthly stacked percentages of positive / neutral / negative, with a sentiment-score line, so you can watch the tone trend.
Filter everything by date range, workspace, questionnaire, question title and sentiment segment to isolate a touchpoint, a question or just the negative comments.
Put it to work
- Catch a souring mood early. A downward bend in the sentiment timeline is a signal to act before it reaches your scores.
- Read the why behind a dip. Filter the verbatim table to the negative segment to see, in customers' own words, what's going wrong.
- Pinpoint the source. Use the question-title filter to find which open-text question is collecting the most negative tone.
- Track participation, not just mood. The comment-rate line tells you whether enough customers are actually leaving feedback to trust the trend.
Tone is the start, not the whole story
Sentiment tells you how customers feel, but not what about. When a band shifts, head to Text Analysis to find the themes driving it — the two reports are designed to be read together.