Top 25+ Affective Computing Applications: Emotion AI Use Cases

Thanks to affective computing, also known as emotion AI, computers start to recognize human emotions based on facial expressions, body language, or voice tone. Technology’s applications in different industries are expanded by significant investment in technology where some managers lack knowledge. As a result, between 2023 and 2027, the market for affective computing is anticipated to increase by more than 35% annually.¹

Below, we have collected 24 applications of affective computing technology from different resources and categorized them under a wide range of business functions and industries.

Marketing

Every marketer, at some stage, hears from some marketing guru that marketing should appeal to basic emotions. Until now, that was a vague, hard-to-measure concept. Now marketers have the ability to put numbers on perceived human feelings as well:

1. Marketing communications

Businesses can analyze what makes their customers engaged and organize their communication strategies accordingly. For example, they can measure physiological responses to their campaigns, products, and services to optimize their marketing strategies.

2. Market research

Emotion AI can measure consumer reactions to new products and help companies understand what other products do well and what they should do to satisfy customers when they enter a new market.

3. Content optimization

Affective technologies can also help businesses generate contents that resonate well with their customers.

If you want to learn how to analyze your data, read our article on sentiment analysis in marketing.

Customer Service

4. Intelligent call routing

Businesses can detect angry customers from the beginning of the call, and such calls can be routed to more experienced and well-trained call agents. 

5. Recommendations during calls

Emotion AI can also provide suggestions about handling customer calls based on similar speech patterns during the conversation.

6. Continuous improvement

Reviews are time-consuming and completed by only a small share of customers. Amazon sellers share that only 1-2% of their buyers leave product reviews.² Like analyzing written reviews, emotion AI can also measure how effective the calls are and if the customer is satisfied at the end of the call by leveraging voice analysis. This data can be used to improve customer service even in cases where customers do not leave reviews.

To find the right AI data partner for your AI projects, check out the following articles:

• Best Data Collection Services & Companies
• Data Crowdsourcing Platform: 10+ Companies & Criteria

You can also check our data-driven list of sentiment analysis services. 

Human Resources

7. Recruitment

Businesses can observe how stressful candidates are and how they communicate emotions during interviews to make better recruitment decisions. Unilever is one of the companies that is currently using emotion AI during job interviews.³ However, this requires interviewee approval for recording the interview, and HR teams shouldn’t rely too much on the accuracy of affective computing as people can express themselves differently.

8. Employee training

Affective computing can be used to train employees who interact directly with customers. Employees work with affective computing systems with emotional intelligence that simulate human emotions to help them improve their empathy and customer service skills.

9. Tracking employee satisfaction

HR teams can track employees’ stress and anxiety levels during the job and observe if they are satisfied with their current tasks and workload. However, it also brings- ethical concerns on monitoring all employees during work hours and might require their consent to monitor their emotional state continuously.

Also read

• AI in HR: Steps & Use Cases with Real-Life Examples.
• HR Analytics Use Cases.
• AI HR Analytics with Benefits & Challenges.

Healthcare

10. Patient care

A bot can be used not only to remind the aging population to take their medications but also to monitor their physical and mental health daily to observe any problematic issues.

11. Medical diagnosis

Affective computing can leverage voice analysis to help doctors diagnose diseases like depression and dementia. Wearable technology with the ability of emotion detection, such as Embrace by Empatica, have already been used by researchers to study stress, autism spectrum disorders, epilepsy, and other disorders.⁴

Figure 1. Affective computing applications in healthcare industry

Source: Affective Computing for Healthcare: Recent Trends, Applications, Challenges, and Beyond⁵

12. Counseling

Emotion AI can be used in counseling sessions to better track and understand mental states and help doctors support counselees more effectively.

13. Treatment

Affective computing can assess emotional data in real time, aiding early diagnosis and personalized treatment. In treatment, affective computing capabilities enhances virtual reality (VR) therapy. VR-based training programs use sensors to track physiological signals and game performance. Machine learning models then analyze this data to assess social skills, emotional responses, and treatment progress. This technology helps address the shortage of autism specialists and improves intervention strategies.⁶

Read our article on sentiment analysis applications in the healthcare industry.

Financial Services

14. Fraud detection

More than 20% of insurers have admitted to lying to their health and car insurance companies to gain coverage in the US.⁷ Insurance companies can leverage voice analysis to prevent such issues and to understand if a customer is lying while submitting a claim.

Similarly, affective computing analyzes voice and tone to assess a borrower’s honesty and moral intent. Applying affective computing in financial services helps lenders make better decisions about approving loans.

15. Investment

In stock investment, emotions influence market trends. Studying investor sentiment from social media, like posts on X (formerly Twitter), helps predict stock price movements. This allows traders to identify human emotions about firms and adjust their strategies.

Retail

16. In-store shopping experience

Emotion AI technology can monitor their customers’ satisfaction levels and reactions while shopping in the store. With the valuable insights gained, retailers can take more effective actions for user satisfaction.

Check our article on how to benefit from sentiment analysis in the retail industry.

Autonomous driving / Driver assistance

17. Safety

Automotive companies can leverage computer vision to track drivers’ emotional states while driving. If the driver is too tired, stressed, or angry/sad, it can provide alerts for unsafe driving.

18. Driving performance

Affective computing systems can also be used to measure autonomous cars’ driving performance. With cameras and microphones embedded in the vehicle, the technology can monitor the passengers’ emotional state and observe if they seem stressed or satisfied with the driving experience.

Education

19. Measuring effectiveness

Sensors like video cameras or microphones can be used for students’ emotional states during lessons. Emotion AI can assess how satisfied or frustrated students are with the lessons because a task is too challenging or too simple. As a result, teachers can adapt themselves to tailor class load accordingly. A similar approach can also be used while testing learning software prototypes for online learning.

20. Supporting autistic children

Another use case in education is to help autistic children recognize other people’s emotions in the school environment.

Gaming

21. Testing

Before releasing their games to the market, gaming companies can use affective computing for testing their games. Emotion AI can monitor players’ satisfaction levels, and businesses can improve further to increase player satisfaction.

22. Adaptive games

Affective computing can leverage computer vision to detect the player’s facial expressions, and the game can adapt to that mental state.

Government

23. Understanding the general mood of the population

The rise of emotion AI also created new partnerships between technology vendors and surveillance camera providers. The Ministry of Happiness in the United Arabic Emirates has started an initiative to understand the general mood of the population using video analysis cameras in public places.⁸

24. Tracking/estimating citizen reactions

Governments or political candidates can monitor social media to measure their population’s response to policy proposals and announcements. Political campaigns can also personalize their messages using psychometric models to optimize the emotional reaction of voters. Emotional AI book shares that emotion AI was used as a sentiment analysis tool by Cambridge Analytica in the 2016 US presidential elections.⁹

Tech

25. Integration with IoT

Emotion AI can be integrated into IoT and other smart devices so that these devices can act based on users’ emotional states detected via voice and face analysis. For example, if the customer seems too sweaty, a smart air conditioner might turn on automatically.

Art

Affective computing bridges computer science and art by analyzing emotions in music and poetry.¹⁰

26. Music

Affective computing systems improve recommendations by matching songs to user emotions, not just titles or lyrics. This enhances the accuracy of music sentiment analysis and helps users discover songs that fit their moods.

27. Poetry

AI-powered tools like deep learning models generate emotionally expressive verses. Traditional methods relied on statistical models, but newer approaches, such as RNN-based generators and generative adversarial networks (GANs), create poetry that captures both form and feeling. These advancements help AI-generated poetry feel more human-like and emotionally engaging.

Other

28. Workplace design

Businesses can track their employees in the workplace and conduct sentiment analysis in internal social networks and forum messages to improve physical workspace design and comfort.

Further Readings

• Top 10 Emotional AI Examples & Use Cases
• What is Affective Computing / Emotion AI
• Affective Computing: In-Depth Guide to Emotion AI

External Links

• 1. Affective Computing Market Size, Share, Trends & Forecast.
• 2. A Deep Dive into Amazon Consumer Review Statistics. Push-Pull
• 3. AI used for first time in job interviews in UK to find best applicants. The Telegraph
• 4. Embrace2 Seizure Monitoring | Smarter Epilepsy Management | Embrace Watch | Empatica. Empatica
• 5. Liu, Y., Wang, K., Wei, L., Chen, J., Zhan, Y., Tao, D., & Chen, Z. (2024). Affective Computing for Healthcare: Recent Trends, Applications, Challenges, and Beyond. arXiv preprint arXiv:2402.13589.
• 6. Liu, Y., Wang, K., Wei, L., Chen, J., Zhan, Y., Tao, D., & Chen, Z. (2024). Affective Computing for Healthcare: Recent Trends, Applications, Challenges, and Beyond. arXiv preprint arXiv:2402.13589.
• 7. 2024 Insurance Dishonesty Report - NerdWallet.
• 8. Empath in UAE to Measure Happiness - INDEX Holding. INDEX Holding
• 9. McStay, A. (2018). Emotional AI: The rise of empathic media.
• 10. Pei, G., Li, H., Lu, Y., Wang, Y., Hua, S., & Li, T. (2024). Affective Computing: Recent Advances, Challenges, and Future Trends. Intelligent Computing, 3, 0076.