AI Use Case: Emotional Analysis using AI and implementing it using Data Machines

Emotional analysis, powered by artificial intelligence (AI), has emerged as a cutting-edge technology that goes beyond simple sentiment analysis. It involves the detection and understanding of complex human emotions expressed in text, speech, or even images. This sophisticated capability has paved the way for numerous practical applications across various domains.Emotion

What is Emotional Analysis?

Unlike Sentiment Analysis where the outcome of the analysis is grouped into a binary notation of Positive or Negative, Emotion Analysis provides more depth to the emotions being expressed by the person. The output provides a range of emotions such as Anger, Disgust, Fear, Joy and Sadness along with a numerical score indicating the depth of each emotion. The sum of all the scores of the emotions aggregate to 100. This provides an insight into all the emotions expressed by users contained in the statements.

Where is Emotional Analysis useful?

Customer Experience Enhancement

One key area where emotional analysis shines is in customer experience enhancement. By analyzing customer interactions, businesses can gain insights into the emotional states of their customers. This allows companies to tailor their services and communication strategies to better meet the emotional needs of their clientele, ultimately improving customer satisfaction and loyalty.

Mental Health

In the realm of mental health, emotional analysis is proving to be a valuable tool. AI-powered applications can analyze written or spoken language to detect signs of emotional distress or mental health issues. This allows for early intervention and support, helping individuals receive timely assistance.


In education, emotional analysis can be utilized to gauge student engagement and well-being. By analyzing students’ written assignments or even facial expressions in virtual classrooms, educators can identify signs of stress or disengagement, allowing for personalized support and intervention.


Job recruitment is another area benefiting from emotional analysis. By evaluating candidates’ responses during interviews or analyzing their written communication, AI can provide insights into their emotional intelligence, helping employers make more informed hiring decisions.

Automotive Industry

Emotional analysis is also making waves in the automotive industry. In-car AI systems can monitor drivers’ emotional states through voice analysis and facial recognition. This ensures a safer driving experience by alerting drivers who may be showing signs of distraction or fatigue.


Moreover, the entertainment sector leverages emotional analysis to enhance user experiences. Streaming platforms use AI to analyze viewer reactions to content, allowing for personalized recommendations based on emotional preferences.

Operational Challenges

While the benefits of Emotional Analysis are obvious, the practical challenges of implementing are many. The key to leveraging the value of Emotional Analysis is in the time taken to respond to any of the signals emitted in the outcome. 

Any system that is being designed to leverage the benefits of Emotional Analysis should be able to embed the AI capability into the sequence of events where emotions need to be observed. Once an analysis is made and the emotions are extracted, a robust notification system is necessary to ensure that the detected emotions are not lost and get the benefit of attention by the responsible party.

The Qualetics AI Platform and Data Machines provides such a capability to both monitor the sequence of events in any application and also perform an Emotional Analysis for the all suitable events and route the information to the desired recipient to take the appropriate action.

For example, in a Customer Support Q&A scenario where a user is interacting with an AI chatbot, it is more than possible that a customer might get frustrated with the responses if it doesn’t meet their expectations. Instead of prolonging such a conversation, the Emotional Analysis Data Machine can be used to constantly analyze the Q&A interactions and programmed to route the user to a Live agent if it detects an emotion such as Anger, Frustration or Sadness.

Let’s see how such an automation can be implemented.

Building a Emotional Analysis AI Solution using a Data Machine

  1. Click on the Data Machines navigation menu in the left navigation
  2. Click on Add Data Machine
  3. Drag an Operational Step from the toolbox
  4. Select “Natural Language Processing Models” in the category
  5. Select “Emotion Analysis” in the list of models
  6. Drag and Drop the Final Step from the toolbox
  7. Configure the options in the Final step based on your need
  8. Test the Data Machine using a statement like – “I hate this place. I want to go back home!”
  9. Review the results produced by the Data Machine and change the statement as needed.
  10. Publish the Data Machine, if the Test is successful
  11. Now the Data Machine is ready to be integrated with any application which might involve users providing an input. Integrate the Data Machine using the available Rest API options or No Code options using Zapier.

Checkout the video below to see the steps in action.

Emotional analysis powered by AI is a transformative technology with wide-ranging practical applications. The ability to understand and respond to human emotions opens up new possibilities for creating more empathetic and personalized interactions across various domains.

Qualetics Data Machines provides the shortest path possible to embed emotion analysis engines into any software application helping businesses accelerate their adoption of AI and Machine Learning based innovations to increase customer satisfaction, engagement, user retention and revenue generation.

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