Glossary
What is sentiment analysis?
Definition
Sentiment analysis is the use of AI to determine the emotional tone behind a piece of text or speech. It classifies input as positive, negative, or neutral, and sometimes detects specific emotions like frustration. In voice systems it helps gauge how a caller feels during a conversation.
01How sentiment analysis works
Text-based sentiment analysis examines word choice, phrasing, and context to assign an emotional label, often using machine learning models trained on labeled examples. In speech, systems may also analyze acoustic cues such as tone, pitch, and pace alongside the transcript. The output can be a single label or a score along a scale.
02Sentiment analysis on calls
During a phone conversation, sentiment signals can indicate that a caller is upset or confused, prompting the system to adjust its approach or escalate to a human. After the call, aggregated sentiment helps businesses spot recurring issues or measure satisfaction trends. It is one input among several and works best combined with intent and context.
03Limitations
Sentiment is subtle, and sarcasm, mixed feelings, or cultural differences can be misread. Short or ambiguous utterances give the model little to work with, and audio quality can obscure vocal cues. Because of this, sentiment output is usually treated as a helpful signal rather than a definitive judgment.
Frequently asked questions
Can sentiment analysis detect anger in a caller's voice?
Systems that analyze acoustic cues can flag signs of frustration or anger, but they are not perfect and may misread subtle or ambiguous speech.
Is sentiment analysis always accurate?
No. Sarcasm, mixed emotions, and poor audio can lead to errors, so results are best used as one signal among several rather than a final verdict.
Related terms
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