Sentiment Analysis
"Sentiment Analysis is the detection of a customer's emotional state, such as frustration, from what they write or say, used to adjust replies and handoff."
What is a Sentiment Analysis?
Sentiment Analysis is the detection of a customer's emotional state, such as frustration, from what they write or say, used to adjust replies and handoff. Related product: Chat Agents.
Reviewed by Atul Diwedi, Co-Founder · Last reviewed
Deep Dive
Sentiment analysis gives a voice or chat agent a reading on how the conversation is going, separate from what is being asked. The same question, 'where is my order', can arrive calm or furious, and the right next step differs. In the agent it runs alongside intent recognition on each turn and feeds two places: the reply the agent writes next, and the rules that decide when a person should take over.
How it works. The model, or a smaller classifier, scores each message and the conversation as a whole on a scale from negative to positive, often with extra labels such as urgent or confused. Inputs are the text (or the transcript on a call, sometimes with acoustic cues such as volume and speech rate), plus the trajectory: a third message that is angrier than the first matters more than a single sharp word. Outputs are a per-turn label, a running conversation score and events the workflow can act on, such as 'negative for two consecutive turns'. The label is also written to the conversation record so it can be searched later.
Why it matters for retention and escalation. Containment metrics look good right up until a customer is trapped in a polite loop with an agent that cannot solve their problem. Sentiment is the early warning. The agent that offers a person after the second frustrated message keeps the customer; one that offers a fourth FAQ answer loses them and earns a public complaint. On the positive side, a satisfied ending is the right moment to ask for a review or offer the upsell. For supervisors it turns a thousand transcripts into a short list worth reading.
Example. A SaaS support chat agent is helping a customer who cannot export a report. The first two messages are neutral; the agent sends the export steps. The third message reads, 'I've done that twice, this is wasting my morning.' The sentiment score drops, the workflow rule fires, and the agent changes course: it acknowledges the time lost, states that it is bringing in a person, and hands off with the full transcript so the customer does not repeat anything. The ticket is tagged negative, and the support lead sees it in the morning review alongside two others about the same export bug.
Sentiment analysis vs intent recognition. Intent is what the person wants; sentiment is how they feel about it. They are independent: a cancellation request can be perfectly calm, and a simple hours question can be furious after three transfers. Routing uses intent; escalation timing and tone use sentiment. Conflating them produces agents that treat every angry message as a complaint intent and every complaint as angry.
What to measure.
| Metric | Why |
|---|---|
| Negative-turn to handoff time | How many turns an unhappy customer waits before a person is offered |
| Escalation precision | Share of sentiment-triggered handoffs a reviewer agrees were needed |
| False negatives | Angry transcripts the classifier scored neutral, found in sampling |
| Sentiment by intent and channel | Where frustration clusters, which is usually a product or policy issue |
Botcadence surfaces sentiment on chat and call records in conversation analytics so handoff rules and weekly reviews can act on it, for chat agents and voice.
Related Technology
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Common Questions
Sentiment Analysis FAQs
Noisy. 'Fine.' and 'great' can be sincere or sarcastic, and one-word replies carry little signal. Treat single-turn scores as weak and act on trajectories: two or more negative turns, or an explicit request for a person, which should always win.
Yes, on the transcript and, with some providers, on acoustic cues such as volume and interruptions. Transcript-only scoring misses tone of voice, so voice agents should also treat repeated barge-ins and repeated questions as frustration signals.
No. It should shorten the path: fewer clarifying questions, a direct answer, and an offer of a person. Automatic transfer on every negative word floods your team with calls the agent could have resolved and makes the metric useless.
Yes. Directness that reads as rude in one language is normal in another, and models trained mostly on English drift on others. Calibrate thresholds per language by reviewing a sample of flagged and unflagged conversations in each.