T Textura

Glossary term

What is sentiment analysis?

Sentiment analysis is the automatic determination of the emotional coloring of a conversation: positive, negative or neutral. It shows how participants feel about the subject of the conversation and how their mood shifts over the course of the dialogue.

Sentiment analysis assesses both the content of speech (which words and phrasings are used) and, in some cases, how it sounds (intonation, pace, volume). The result can be an overall score for the conversation or its trajectory — a trend from start to finish — which shows whether tension was defused or, conversely, the conversation worsened.

The sentiment of a single call is useful, but the real value is in aggregation across the whole volume. Trends of negativity by topic, agent, time and channel expose systemic problems: a specific scenario consistently causes dissatisfaction, a certain topic correlates with irritation, some agents' conversations more often end on a negative note.

For a business, sentiment analysis is an early signal: it helps spot a spike in dissatisfaction in time, prioritize problem contacts and set up alerts on sharply negative conversations. Combined with topics and reasons for contact, sentiment explains not only "how much" negativity there is but "why."

Sentiment analysis is one of the speech-analytics metrics and is closely tied to satisfaction measures. It doesn't replace surveys like NPS and CSAT but complements them: sentiment is computed automatically across 100% of conversations, whereas surveys reflect the opinion of only the responding share of customers.

How it works in the platform

In the platform, sentiment analysis is part of the set of system metrics: each recording is scored with overall sentiment, its trend and tone. These metrics land in the analytical store, are available in filters and on dashboards and can trigger automations — for example, an alert on a sharply negative conversation.

Related terms

Speech analytics Speech analytics is the automatic analysis of conversations (calls and written exchanges) to extract metrics, topics, tone and other data from what was said and how the dialogue unfolded. It turns recordings into structured data you can build dashboards on and make decisions from. NPS NPS (Net Promoter Score) is a metric of customers' willingness to recommend a company, based on the question "how likely are you to recommend us?" on a scale from 0 to 10. It shows customers' overall attitude toward the brand rather than satisfaction with a specific conversation. CSAT CSAT (Customer Satisfaction Score) is a metric of satisfaction with a specific interaction: a purchase, a support contact, a conversation. It's usually measured with the question "how satisfied are you?" on a scale immediately after the contact.

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