Sentiment analysis
What sentiment analysis is, how a business uses it on calls, and where it gets things wrong.
The short answer
What is sentiment analysis?
Sentiment analysis is the automatic detection of the emotional tone in text or speech, usually labeled positive, neutral or negative. In a call center it is applied to call transcripts to flag frustrated or unhappy customers, spot trends across many calls and decide which calls a manager should review first. It reads tone from words and phrasing; it doesn't know why a customer is upset, so a person still needs to read the call.
How it works
After the call, a language model reads the transcript and scores the caller's tone, sometimes for the whole call and sometimes moment by moment, so you can see where a call turned. The result is stored with the call, which lets you filter and sort: all negative calls this week, or the share of negative calls about one topic.
Example
An online store's AI agent answers order-status calls overnight. In the morning the owner opens the list of calls, sorts by sentiment, and starts with the two negative ones: a damaged delivery and a double charge. The forty routine calls can wait, or don't need anything at all.
What it means for a small business
Use sentiment to decide what to read first, not as a score of how good your service is. With Daber AI every call ends with a recording, transcript, summary and sentiment, searchable in one dashboard, so an angry caller stands out in the next morning's list instead of being buried in it.
Questions
How accurate is sentiment analysis?
Good enough to sort calls, not good enough to judge one call alone. Sarcasm, mixed feelings and polite complaints trip it up. Treat it as a signal that points you to a call, then read or listen to it.
Does sentiment analysis work in languages other than English?
Yes, modern language models read tone in many languages, though results vary. Daber AI records sentiment on every call in the language the call was held in.
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