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Emotion Detection and Classification Using Machine Learning Techniques

Emotion Detection and Classification Using Machine Learning Techniques

Amita Umesh Dessai, Hassanali G. Virani
Copyright: © 2023 |Pages: 21
ISBN13: 9781668456736|ISBN10: 1668456737|ISBN13 Softcover: 9781668456743|EISBN13: 9781668456750
DOI: 10.4018/978-1-6684-5673-6.ch002
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MLA

Dessai, Amita Umesh, and Hassanali G. Virani. "Emotion Detection and Classification Using Machine Learning Techniques." Multidisciplinary Applications of Deep Learning-Based Artificial Emotional Intelligence, edited by Chiranji Lal Chowdhary, IGI Global, 2023, pp. 11-31. https://doi.org/10.4018/978-1-6684-5673-6.ch002

APA

Dessai, A. U. & Virani, H. G. (2023). Emotion Detection and Classification Using Machine Learning Techniques. In C. Chowdhary (Ed.), Multidisciplinary Applications of Deep Learning-Based Artificial Emotional Intelligence (pp. 11-31). IGI Global. https://doi.org/10.4018/978-1-6684-5673-6.ch002

Chicago

Dessai, Amita Umesh, and Hassanali G. Virani. "Emotion Detection and Classification Using Machine Learning Techniques." In Multidisciplinary Applications of Deep Learning-Based Artificial Emotional Intelligence, edited by Chiranji Lal Chowdhary, 11-31. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-5673-6.ch002

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Abstract

This chapter analyzes 57 articles published from 2012 on emotion classification using bio signals such as ECG and GSR. This study would be valuable for future researchers to gain an insight into the emotion model, emotion elicitation and self-assessment techniques, physiological signals, pre-processing methods, feature extraction, and machine learning techniques utilized by the different researchers. Most investigators have used openly available databases, and some have created their datasets. The studies have considered the participants from the healthy age group and of similar cultural backgrounds. Fusion of the ECG and GSR parameters can help to improve classification accuracy. Additionally, handcrafted features fused with automatically extracted deep machine learning features can increase classification accuracy. Deep learning techniques and feature fusion techniques have improved classification accuracy.

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