Sentiment analysis uses natural language processing (NLP) models to assign an emotional tone—commonly positive, negative, or neutral—to customer-generated content such as chat messages, emails, survey responses, social posts, or call transcripts. More advanced models produce granular scores or detect specific emotions like frustration, satisfaction, or confusion.
Why it matters: Manual review of customer language doesn't scale. Sentiment analysis lets CX teams monitor emotional trends across thousands of interactions simultaneously, catch at-risk customers before they churn, and measure the impact of product or process changes on overall mood.
Practical examples:
- Flagging a chat session as highly negative so a supervisor can intervene in real time
- Tracking aggregate sentiment scores week-over-week to evaluate a new returns policy
- Correlating negative sentiment spikes with specific agent IDs for targeted coaching
Sentiment scores are often displayed alongside CSAT (customer satisfaction) and NPS (Net Promoter Score) to give a fuller picture of customer perception.