Analysis of sentiment in calls thanks to AI


Smith, Emma

Publish: Wednesday, Jul 03
Analysis of sentiment

Have you ever wished you could read your clients’ minds during a call? Know exactly how they feel and adjust your approach in real time? Thanks to the IA from Fonvirtual, this is no longer a dream. Call sentiment analysis is a powerful tool that can transform the way you interact with your customers. At Fonvirtual, we are at the forefront of this technology, helping you better understand your customers and improve their experience. Analysis of sentiment in calls thanks to AI is an exciting advancement that offers deeper insights into customer emotions during interactions. In this article, you will discover how this new technology works and how you can make the most of it.

What is sentiment analysis?

Sentiment analysis, also known as opinion mining, is the process of identifying and categorizing the opinions expressed in a text (or in the transcript of a conversation, in the case of a call) to determine whether the attitude of the interlocutor towards a particular topic is positive, negative or neutral. This analysis is represented by emoticons on the call panel, so that both the agent and the supervisor can identify in real time the attitude of the agent and the client during conversations.

Although they sound similar, there are actually important differences between sentiment analysis and attitude analysis. Both seek to understand emotional aspects of an interaction; However, sentiment analysis is limited to identifying basic emotions in the text, while attitude analysis offers a more complete vision by incorporating non-verbal and contextual elements of communication.

How does sentiment analysis work? 

He sentiment analysis It uses machine learning and natural language processing algorithms to analyze the language used in a conversation and extract emotional information. 

Call transcript:

  • First, the phone call is converted into text using voice recognition technology (ASR – Automatic Speech Recognition).

Natural Language Processing (NLP):

  • The transcribed text is analyzed using natural language processing algorithms to identify words and phrases that indicate emotions.

Classification of Feelings:

  • Machine learning models or techniques based on dictionaries containing emotionally charged words are applied.
  • These models classify text sentiment as positive, negative, or neutral based on words and context.

Benefits of sentiment analysis on calls

Improved customer experience

Sentiment analysis offers a valuable tool to improve customer experience by providing a quick assessment of the overall emotional tone during interactions. This approach allows:

  • Context-Based Assessment: Agents receive feedback on overall customer perception in real time, based on text analysis of the phone conversation.
  • Interaction Guidance: It makes it easier for agents to adjust their approach and response tone in accordance with the emotional signals detected.
  • Adaptation of Strategies: Allows companies to adapt strategies to optimize interactions, responding more sensitively and effectively to expressed needs.

Custom agent training

Sentiment analysis allows you to filter out calls that have received a positive evaluation from the agent, which can be used to train and train new agents. This involves showing them how to handle difficult situations and how to make effective sales. By strategically using sentiment analysis, the company ensures that they are prepared to offer exceptional customer service based on real examples and positive results from previous interactions.


Sentiment analysis in agent performance management allows supervisors to evaluate customer interactions to identify both the highest and lowest levels of performance. This provides them with insights into specific customer service issues and facilitates the development of personalized improvement strategies. In addition, it allows the implementation of recognition and reward programs aimed at the agents with the best performance, based on objective evaluations of customer satisfaction.

Integration with call center software and areas for improvement

Integrating artificial intelligence with your sentiment analysis software provides valuable data on customers’ emotional perception. This includes identifying patterns of positive, negative, or neutral sentiment in interactions. For example, you can analyze whether there are specific times of the day when customers show predominantly negative reviews, or whether certain products generate more positive reviews. This data helps make informed decisions to continually optimize the customer experience.

Proactive identification of dissatisfied customers

The tool allows supervisors to quickly identify dissatisfied customers during the call and proactively address their issues. This makes it easier to resolve concerns before they become serious problems. For example, if a customer calls to complain about a defective product, sentiment analysis detects the negative emotional charge, alerting the agent to handle the call with greater empathy and offer quick solutions, such as an immediate replacement or refund. In addition, the supervisor can participate in the call and interrupt the conversation, giving the agent instructions on how to act. All this is possible thanks to the faces that change color in real time, which makes it possible to identify whether the agent’s treatment improves or worsens and whether the user is more or less satisfied.

In Fonvirtual, we are here to help you implement this technology and transform the way you interact with customers. Ready to start? 


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