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Deep Learning Approach for Emotion Recognition Analysis in Text Streams

Deep Learning Approach for Emotion Recognition Analysis in Text Streams

Changxiu Liu, S. Kirubakaran, Alfred Daniel J.
Copyright: © 2022 |Volume: 18 |Issue: 2 |Pages: 21
ISSN: 1548-3908|EISSN: 1548-3916|EISBN13: 9781799893653|DOI: 10.4018/IJTHI.313927
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MLA

Liu, Changxiu, et al. "Deep Learning Approach for Emotion Recognition Analysis in Text Streams." IJTHI vol.18, no.2 2022: pp.1-21. http://doi.org/10.4018/IJTHI.313927

APA

Liu, C., Kirubakaran, S., & Alfred Daniel J. (2022). Deep Learning Approach for Emotion Recognition Analysis in Text Streams. International Journal of Technology and Human Interaction (IJTHI), 18(2), 1-21. http://doi.org/10.4018/IJTHI.313927

Chicago

Liu, Changxiu, S. Kirubakaran, and Alfred Daniel J. "Deep Learning Approach for Emotion Recognition Analysis in Text Streams," International Journal of Technology and Human Interaction (IJTHI) 18, no.2: 1-21. http://doi.org/10.4018/IJTHI.313927

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Abstract

Social media sites employ various approaches to track feelings, including diagnosing neurological problems, including fear, in people or assessing a population public sentiment. One essential obstacle for automatic emotion recognition principles is variable with fluctuating limitations, language, and interpretation shifts. Therefore, in this paper, a deep learning-based emotion recognition (DL-EM) system has been proposed to describe the various relational effects in emotional groups. A soft classification method is suggested to quantify the tendency and allocate a message to each emotional class. A supervised framework for emotions in text streaming messages is developed and tested. Two of the major activities are offline teaching assignments and interactive emotion classification techniques. The first challenge offers templates in text responses to describe sentiment. The second activity includes implementing a two-stage framework to identify live broadcasts of text messages for dedicated emotion monitoring.