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Multimodal Emotion Detection with Sentimental Analysis Chatbot
Authors
1 2 3 Department of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Chennai, Tamil Nadu, India.
Article Information
- DOI
- No DOI
- Published In
- Volume 5, Issue 3 (2026)
- Published Online
- September-December 2026
- Pages
- 683-690
- Article Type
- Original Article
- Check for updates
- No DOI available for cross-reference
References
1. S. Poria, E. Cambria, R. Bajpai, and A. Hussain, "A review of affective computing: From unimodal analysis to multimodal fusion," Information Fusion, vol. 37, pp. 98–125, 2017.
2. A. Zadeh, M. Chen, S. Poria, E. Cambria, and L.-P. Morency, "Tensor fusion network for multimodal sentiment analysis," in Proc. EMNLP, Copenhagen, Denmark, 2017, pp. 1103–1114.
3. S. Li and W. Deng, "Deep facial expression recognition: A survey," IEEE Trans. Affective Computing, vol. 13, no. 3, pp. 1195–1215, 2020.
4. G. Trigeorgis et al., "Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network," in Proc. ICASSP, Shanghai, China, 2016, pp. 5200–5204.
5. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, "BERT: Pre-training of deep bidirectional transformers for language understanding," in Proc. NAACL-HLT, Minneapolis, MN, USA, 2019, pp. 4171–4186.
6. D. Hazarika, R. Zimmermann, and S. Poria, "MISA: Modality-invariant and -specific representations for multimodal sentiment analysis," in Proc. ACM MM, Seattle, WA, USA, 2020, pp. 1122–1131.
7. M. Chen et al., "Multimodal transformer for emotion recognition in conversation," IEEE Access, vol. 9, pp. 85020–85031, 2021.
8. S. R. Livingstone and F. A. Russo, "The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS)," PLoS ONE, vol. 13, no. 5, p. e0196391, 2018.
9. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.
10. K. Zhang, Y. Huang, Y. Du, and L. Wang, "Facial expression recognition based on deep evolutional spatial-temporal networks," IEEE Trans. Image Processing, vol. 26, no. 9, pp. 4193–4203, 2017.
11. S. Poria et al., "Context-dependent sentiment analysis in user-generated videos," in Proc. ACL, Vancouver, Canada, 2017, pp. 873–883.
12. N. Majumder, S. Poria, D. Hazarika, R. Mihalcea, A. Gelbukh, and E. Cambria, "DialogueRNN: An attentive RNN for emotion detection in conversations," in Proc. AAAI, Honolulu, HI, USA, 2019, pp. 6818–6825.
13. T. Mittal, U. Bhatt, R. Chandra, A. Bera, and D. Manocha, "EmotiCon: Context-aware multimodal emotion recognition using Frege's principle," in Proc. CVPR, Seattle, WA, USA, 2020, pp. 14234–14243.
14. E. Cambria, Y. Li, F. Z. Xing, S. Poria, and K. Kwok, "SenticNet 7: A commonsense-based neurosymbolic AI framework for explainability," in Proc. AAAI, 2022, pp. 5920–5929.
15. A. Bagher Zadeh, P. P. Liang, S. Poria, E. Cambria, and L.-P. Morency, "Multimodal language analysis in the wild: CMU-MOSEI dataset and interpretable dynamic fusion graph," in Proc. ACL, Melbourne, Australia, 2018, pp. 2236–2246.
2. A. Zadeh, M. Chen, S. Poria, E. Cambria, and L.-P. Morency, "Tensor fusion network for multimodal sentiment analysis," in Proc. EMNLP, Copenhagen, Denmark, 2017, pp. 1103–1114.
3. S. Li and W. Deng, "Deep facial expression recognition: A survey," IEEE Trans. Affective Computing, vol. 13, no. 3, pp. 1195–1215, 2020.
4. G. Trigeorgis et al., "Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network," in Proc. ICASSP, Shanghai, China, 2016, pp. 5200–5204.
5. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, "BERT: Pre-training of deep bidirectional transformers for language understanding," in Proc. NAACL-HLT, Minneapolis, MN, USA, 2019, pp. 4171–4186.
6. D. Hazarika, R. Zimmermann, and S. Poria, "MISA: Modality-invariant and -specific representations for multimodal sentiment analysis," in Proc. ACM MM, Seattle, WA, USA, 2020, pp. 1122–1131.
7. M. Chen et al., "Multimodal transformer for emotion recognition in conversation," IEEE Access, vol. 9, pp. 85020–85031, 2021.
8. S. R. Livingstone and F. A. Russo, "The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS)," PLoS ONE, vol. 13, no. 5, p. e0196391, 2018.
9. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.
10. K. Zhang, Y. Huang, Y. Du, and L. Wang, "Facial expression recognition based on deep evolutional spatial-temporal networks," IEEE Trans. Image Processing, vol. 26, no. 9, pp. 4193–4203, 2017.
11. S. Poria et al., "Context-dependent sentiment analysis in user-generated videos," in Proc. ACL, Vancouver, Canada, 2017, pp. 873–883.
12. N. Majumder, S. Poria, D. Hazarika, R. Mihalcea, A. Gelbukh, and E. Cambria, "DialogueRNN: An attentive RNN for emotion detection in conversations," in Proc. AAAI, Honolulu, HI, USA, 2019, pp. 6818–6825.
13. T. Mittal, U. Bhatt, R. Chandra, A. Bera, and D. Manocha, "EmotiCon: Context-aware multimodal emotion recognition using Frege's principle," in Proc. CVPR, Seattle, WA, USA, 2020, pp. 14234–14243.
14. E. Cambria, Y. Li, F. Z. Xing, S. Poria, and K. Kwok, "SenticNet 7: A commonsense-based neurosymbolic AI framework for explainability," in Proc. AAAI, 2022, pp. 5920–5929.
15. A. Bagher Zadeh, P. P. Liang, S. Poria, E. Cambria, and L.-P. Morency, "Multimodal language analysis in the wild: CMU-MOSEI dataset and interpretable dynamic fusion graph," in Proc. ACL, Melbourne, Australia, 2018, pp. 2236–2246.
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