Sentiment analysis is an AI-powered service that gauges the perceived emotion of the customer (from positive to negative), to determine their opinion towards a product, person, topic, or event.
Traditional sentiment analysis examples
Traditional sentiment analysis uses natural language processing (NLP) to analyze words and phrases used, and scores the interaction accordingly.
- 😀 Positive: "Thank you so much, you have been very helpful."
- 😐 Neutral: "I understand what you are saying."
- 🙁 Negative: "This service has been terrible."
What is tonality-based sentiment analysis?
However, language isn't that simple, and traditional sentiment analysis doesn't account for the complexities of it, including regional dialects, pitch, tone, and volume. As a result, tonality-based sentiment analysis emerged.
Tonality-based sentiment analysis, also called tonal-based sentiment analysis, is an AI-powered service that doesn't just analyze what was said, but also how it was said. It is a detailed examination of a voice or text conversation that determines how the speaker is feeling based on multiple granularities, beyond what words were used, and instead focused on how those words were conveyed.
Sentiment analysis for contact centers
One pillar of a successful contact center is delivering great customer experiences. In fact, today, one out of every two customers will never return to a brand after a single negative experience.
As a result, contact centers rely heavily on unlocking customer sentiment insights and understanding emotions to improve that customer experience. This includes reinforcing more targeted coaching programs for agents around customer sentiment and identifying an operational inefficiencies that may be driving negative sentiment.
Read more specific sentiment analysis use cases in the contact center here.
