Voice Sentiment Analysis model

Hi, exploring around? I’m Conviner, your call center terminology assistant, ready to help you learn more about contact centers. 

Did you know? According to one guide, “Around 41% of sales professionals use AI to better understand customer sentiment and emotions.

1. What is the Voice Sentiment Analysis model?

A voice sentiment analysis model uses artificial intelligence to detect emotions and opinions in spoken conversations. It analyzes tone, pitch, pace, and choice of words to determine whether a customer’s sentiment is positive, negative, or neutral. Platforms like Convin use voice sentiment analysis to help contact centers identify frustrated customers, improve coaching, and enhance CX in real time.

2. What is the main purpose of Sentiment Analysis?

The main purpose of sentiment analysis is to understand how customers feel about a product, service, or experience. By classifying opinions as positive, negative, or neutral, businesses can make data-driven decisions that improve engagement and loyalty. Voice sentiment analysis extends this by reading tone and emotion, not just text, to deliver richer context.

3. What are the three types of Sentiment Analysis?

The three types of sentiment analysis are:

1. Fine-grained Analysis – Measures emotion intensity (e.g., very happy, slightly upset).
   
   
2. Aspect-based Analysis – Evaluates sentiment toward specific topics (e.g., product quality, support).
   
   
3. Intent-based Analysis – Detects the reason behind a customer’s emotion, such as intent to cancel or purchase.

Voice sentiment models often combine these to give a complete emotional picture.

4. What is a real-life example of Sentiment Analysis?

A real-life example of sentiment analysis is a contact center using AI to detect a caller’s frustration during a support call. Convin’s Voice Sentiment Analysis automatically flags such calls and provides insights for coaching and escalation, helping improve customer satisfaction and agent performance.

Read Convin’s guide on voice sentiment analysis and transform your contact-centre QA now