Sentiment Analysis

Sentiment Analysis helps you understand how respondents feel based on what they say during the conversation.

How Sentiment Works#

Completed responses include sentiment labels generated from the conversation summary.

Sentiment values:

Positive: Satisfaction, praise, or enthusiasm
Negative: Frustration, disappointment, or criticism
Neutral: Factual or balanced feedback without strong emotion
Mixed: Both positive and negative feedback

The AI considers tone, context, and the meaning of the answer rather than exact keywords alone.

Confidence Levels#

Per-answer summaries can include a confidence level:

High
Medium
Low

Confidence is a label, not a percentage.

Distribution and Trend#

The Analytics Dashboard shows sentiment in two ways:

Distribution#

Shows the overall breakdown of positive, negative, neutral, and mixed completed responses for the selected period.

Trend#

Shows how sentiment changes over time within the selected period.

When previous-period data exists, the dashboard can compare the current period with the previous period of equal length.

Per-Response and Per-Answer Sentiment#

Overall Response Sentiment#

Each completed response can have one overall sentiment label in the response list.

Per-Answer Sentiment#

Inside a response, each summarized answer can have its own sentiment and confidence level. This helps you see which questions or topics drive positive or negative reactions.

Using Sentiment Data#

Find Negative Feedback Quickly#

Filter responses by negative sentiment to review pain points and urgent issues.

Measure Product Changes#

Use date filters to compare sentiment before and after a launch, pricing change, or onboarding update.

Investigate a Topic#

Use response search to find a product area, issue, or phrase, then read the matching summaries and transcripts for context.

Validate With Responses#

Analytics point to patterns. Open individual responses to read the summary, quotes, and message history behind those patterns.