Contact Center AI Sentiment Analysis for Tele Callers & Customers

For voice calls, the system generally converts speech into text and analyzes the words, phrases, and conversation context. SAN Softwares Contact Center Sentiment Analysis also analyze vocal characteristics such as pace, pitch, volume, interruptions, and pauses. The results can be used to monitor customer sentiment, agent sentiment, or both.

Yes, SanAI- Contact Center Sentiment Analysis detects both customer and agent’s emotions. Speaker separation allows the conversation to be analyzed by participants, so the system can compare the customer’s emotional state with the agent’s tone, language, and response. This can help identify whether the interaction is improving or not.

It can flag interactions with strongly negative sentiment, declining sentiment, or unusual customer–agent sentiment patterns. Your teams can review those interactions in detail instead of relying only on random auditing samples. The sentiment result should support QA review, not replace human judgment.

Yes. In a live interaction, sentiment can be updated as new speech or messages are processed. A supervisor may receive an alert when customer sentiment becomes highly negative or when the conversation shows signs of deterioration. The practical value depends on model accuracy and the organization’s response process.

A dashboard can show sentiment by customer, agent, team, channel, call reason, product, location, and time. Useful views include sentiment trends, calls requiring review, customer, agent sentiment comparison, escalation, key indicators, key suggestions to improve, agent score and links to the relevant transcript or recording.