Sentiment Analysis (تحليل المشاعر)
Sentiment analysis uses AI to detect the emotion behind text or speech — positive, negative, or neutral. In customer service it scores every call or message in real time, flagging angry customers for priority handling and revealing trends across thousands of interactions.
In Iraqi dialect
يگيش إذا الزبون مبسوط أو معصّب من صوته
In detail
Sentiment analysis applies natural-language processing to measure attitude and emotion. It classifies individual statements and aggregates them into trends. In a call center, it listens to tone, word choice, and pace to detect frustration the moment it appears, letting supervisors intervene or route the caller to a senior agent. Over time it turns subjective feedback into dashboards: which products draw complaints, which scripts calm callers, and how satisfaction shifts after a change. Combined with speech-to-text, it makes every conversation searchable by emotion, not just by keyword.
Practical example
A support dashboard turns red when sentiment analysis detects a spike in negative calls about a service outage.
Frequently asked questions
How is sentiment analysis used in call centers?
It scores caller emotion live, escalates upset customers, and aggregates feedback into satisfaction trends.
Can it analyze spoken emotion, not just words?
Yes — advanced systems combine word meaning with tone, pitch, and pace for a fuller emotional read.
Related terms
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