Sentiment Analysis for Ideas and Comments

Sentiment Analysis uses AI to classify text as positive, negative, neutral, or mixed in tone, pairing each classification with a confidence score and rolling results up into trend visualizations. This gives program managers a faster way to understand how a community feels about specific topics, individual ideas and comments without reading every submission manually.

How Sentiment Analysis Works

Sentiment Analysis runs automatically on the text content of ideas and comments across the community. Each piece of text is analyzed by AI to determine its emotional tone, and the result is stored alongside the idea or comment as a sentiment classification with an associated confidence score. No manual tagging is required; analysis happens as ideas and comments are submitted.

2. Sentiment Classification

Sentiment Analysis runs automatically on the text content of ideas and comments across the community. Each piece of text is analyzed by AI to determine its emotional tone, and the result is stored alongside the idea or comment as a sentiment classification with an associated confidence score. 

Each analyzed idea or comment is assigned one of the following sentiment values, based on its polarity score:

a) Positive - Assigned when the polarity score is higher.
b) Negative -  Assigned when the polarity score is lower.
c) Neutral - Assigned when the polarity score stays near zero.
d) Mixed - Assigned when the polarity score is very close to both positive and negative.

3. Display of Sentiment Analysis

Sentiment Analysis results for ideas and comments are displayed in the following locations:

• Moderation Dashboard
• Idea Portfolio

Moderation Dashboard


Idea Portfolio

4. Visibility of Sentiment Analysis

Sentiment Analysis is visible to Workspace Administrators, Community Administrators, and Moderators.