AI Sentiment Analysis

Sera AI calculates sentiment for requests, giving specialists visibility into how a user is feeling about their support experience. Sentiment analysis evaluates the emotional tone of the conversation and tracks shifts as the request progresses.

How sentiment works

Sera AI evaluates the notes and messages associated with a request to determine the overall sentiment. It categorizes sentiment as positive, neutral, or negative, and identifies transitions in tone across the conversation lifecycle.

Sentiment appears in real time. When a request is created (for example, through the Virtual Agent), sentiment analysis runs immediately on the initial conversation content.

Where sentiment appears

The sentiment indicator is displayed on the request record alongside the request details. It includes a visual indicator and a text description of the overall sentiment trajectory.

For example, a conversation might start with a user expressing frustration about an access issue (slightly negative sentiment), then shift to positive sentiment after a specialist responds quickly and resolves the problem. Sera AI captures this progression and presents the overall trajectory.

How specialists use sentiment

Sentiment analysis helps specialists in practical ways:

• Tone awareness: A specialist scanning a request quickly might be about to respond in a neutral tone, but seeing a negative sentiment indicator prompts them to spend more time understanding the requester's perspective and crafting a more empathetic response.
• Prioritization: When a specialist has multiple requests with similar resolution targets in their inbox, sentiment can help them decide which to address first. Requests with negative or declining sentiment may benefit from more urgent attention.
• Quality review: Managers can identify interactions where sentiment improved significantly, highlighting effective specialist behaviors worth replicating.

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