Sentiment analysis
Sentiment analysis, sometimes also called opinion mining or emotional AI, uses natural language processing, text mining, computational linguistics, and biometrics to systematically identify, extract, quantify, and study emotional states and subjective information. Sentiment analysis is widely applied to reviews, survey responses, online networks, social networks, and more.
On this page
Source content checked . English translation published 2026-10-27.
In business automation
Sentiment analysis attempts to classify the tone of text as positive, negative, or neutral. In automation, it can help prioritize inquiries, but it does not replace reading the content and should not close an inquiry on its own.
Examples
These examples illustrate a possible workflow. Adapt the connections and rules to your own systems.
Prioritizing an inquiry queue
Chat inquiries are classified by tone. An inquiry flagged as angry moves to the top of the agent's queue. The system does not send an automatic apology before the agent has seen the full context.
Relevant tools
- AssemblyAI
- It offers speech and text understanding capabilities; sentiment classification still needs to be checked against real business conversations.
What to check
- Sarcasm, mixed Hebrew, and quotations can make a positive classification completely wrong.