News and Announcements
RESEARCH ARTICLE
A personalized model for joint dialogue emotion classification and act recognition
Show Less
1 Department of Intelligent Science and Technology, College of Data Science and Application, Inner Mongolia University of Technology, Hohhot, Inner Mongolia,
China
2 Department of Information Engineering, Xuchang Electric Vocational College, Xuchang, Henan,
China
3 Department of Intelligent Science and Technology, Inner Mongolia Autonomous Region Engineering & Technology Research Center of Big Data Based Software Service, Inner Mongolia University of Technology, Hohhot, Inner Mongolia,
China
HPR, 0401 https://doi.org/10.36922/hpr.0401
Received: 24 November 2025 | Revised: 16 February 2026 | Accepted: 27 February 2026 | Published online: 25 May 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Abstract
Background
Joint dialogue emotion classification and act recognition aim to simultaneously predict emotional and behavioral labels for each utterance. While existing models leverage discourse context, they largely overlook individual differences in sequential expression patterns.
Objective
This paper proposes a personalized model integrating personality for joint dialogue emotion classification and act recognition (PEDR-GAT) to incorporate personality traits into both local semantics and global temporal reasoning, thereby bridging inference gaps caused by neglected individual differences.
Methods
First, personality information and label distributions were used to initialize utterance representations, strengthening the association between personality-guided semantics and task labels. Second, graph attention mechanisms extracted global temporal dependencies across utterances and captured cross-task interactions. Finally, a co-attention mechanism integrated personality-guided local and global features to maintain long-term dependencies and supplement short-term contextual information.
Results
Experiments on Mastodon, DailyDialog, and CPED datasets show that PEDR-GAT outperforms baselines, achieving F1 improvements of 2.0%/0.6%, 2.3%/1.9%, and 5.3%/2.3% for emotion/act recognition, respectively. Personality-guided semantic label acquisition enhances semantics-label associations, and personality-fused graph attention effectively captures latent temporal patterns, demonstrating the value of incorporating individual differences into joint dialogue modeling.
Conclusion
PEDR-GAT effectively integrates personality traits into joint dialogue emotion and act recognition, demonstrating that modeling individual differences significantly enhances performance across multiple datasets.
Keywords
Personality
Graph attention mechanisms
Dual-task recognition
Multi-head attention
Funding
This work was supported by the National Natural Science Foundation of China (No. 62166029); the Research Infrastructure and Platforms in the Inner Mongolia Autonomous Region (No. 2025KYPT0014); the Inner Mongolia Autonomous Region Higher Education Innovation Team Development Plan (NO. NMGIRT2506); the Science and Technology Plan Project of Inner Mongolia Autonomous Region (No. 2025SYFHH1239); the Philosophy and Social Sciences Planning Project of Xuchang City (No.XCSK20260715).
Conflict of interest
The authors declare no competing interests.
References
- Cerisara C, Jafaritazehjani S, Oluokun A, Le HT. Multi-task dialog act and sentiment recognition on Mastodon. In: Proceedings of the 27th International Conference on Computational Linguistics. 2018;745–754.
- Kim M, Kim H. Integrated neural network model for identifying speech acts, predicators, and sentiments of dialogue utterances. Pattern Recognition Letters. 2017;101:1- 5. doi: 10.1016/j.patrec.2017.11.009
- Qin L, Che W, Li Y, Ni M, Liu T. DCR-NET: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2020;34(05):8665-8672. doi: 10.1609/aaai.v34i05.6391
- Poria S, Majumder N, Mihalcea R, Hovy EH. Emotion recognition in conversation: research challenges, datasets, and recent advances. IEEE Access. 2019;7:100943-100953.
- Xing B, Tsang I. DARER: Dual-task Temporal Relational Recurrent Reasoning Network for joint Dialog Sentiment Classification and ACT recognition. In: Proceedings of the Findings of the Association for Computational Linguistics: ACL 2022. 2022;3611-3621. doi: 10.18653/v1/2022.findings-acl.286
- Zhang Y, Zhao G, Shu Y, et al. CPED: a Chinese Positive Emotion Database for emotion Elicitation and analysis. IEEE Trans Affect Comput. 2021;14(2):1417-1430. doi: 10.1109/taffc.2021.3088523
- Xu Y, Yao E, Liu C, Liu Q, Xu M. A novel ensemble model with two-stage learning for joint dialog act recognition and sentiment classification. Pattern Recognition Letters. 2022;165:77-83. doi: 10.1016/j.patrec.2022.11.032
- Li S, Yan H, Qiu X. Contrast and generation make BART a good dialogue emotion recognizer. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2022;36(10):11002-11010. doi: 10.1609/aaai.v36i10.21348
- Ayetiran EF. Attention-based aspect sentiment classification using enhanced learning through cnn-Bilstm networks. Knowl-Based Syst. 2022;252:109409. doi: 10.1016/j.knosys.2022.109409
- Majumder N, Poria S, Hazarika D, Mihalcea R, Gelbukh A, Cambria E. DialogueRNN: an attentive RNN for emotion detection in conversations. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2019;33 (1):6818- 6825.
- Hu D, Wei L, Huai X. DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing. 2021;1: 7042–7052.
- Kumar H, Agarwal A, Dasgupta R, Joshi S. Dialogue act sequence labeling using hierarchical encoder with CRF. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence (AAAI’18/IAAI’18/EAAI’18). AAAI Press. 2018;421:3440– 3447.
- Raheja V, Tetreault J. Dialogue Act Classification with Context-Aware Self-Attention. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2019;1 (Long and Short Papers):3727–3733.
- Duran N, Battle S, Smith J. Sentence encoding for Dialogue Act classification. Nat Lang Eng. 2021;29(3):794-823. doi: 10.1017/s1351324921000310
- Malhotra G, Waheed A, Srivastava A, Akhtar MS, Chakraborty T. Speaker and time-aware joint contextual learning for dialogue-act classification in counselling conversations. In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. 2022;735-745. doi: 10.1145/3488560.3498509
- Zheng L, Li F, Chai Y, Teng C, Ji D. A bi-directional multi-hop inference model for joint dialog sentiment classification and ACT recognition. In: Liu, F., Duan, N., Xu, Q., Hong, Y. (eds) Proceedings of the Natural Language Processing and Chinese Computing. NLPCC 2023. Lecture Notes in Computer Science. 2023;14302. doi: 10.1007/978-3-031-44693-1_19.
- Li J, Fei H, Ji D. Modeling local contexts for joint dialogue act recognition and sentiment classification with bi-channel dynamic convolutions. In: Proceedings of the International Conference on Computational Linguistics. 2020;616-626.
- Ghosal D, Majumder N, Poria S, Chhaya N, Gelbukh A. DialogueGCN: a graph convolutional neural network for emotion recognition in conversation. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2019;154-164.
- Zhang D, Wu L, Sun C, Li S, Zhu Q, Zhou G. Modeling both context- and speaker-sensitive dependence for emotion detection in multi-speaker conversations. In: Proceedings of the International Joint Conference on Artificial Intelligence. 2019;5415-5421.
- Zhao W, Zhao Y, Qin B. MuCDN: mutual conversational detachment network for emotion recognition in multi-party conversations. In: Proceedings of the International Conference on Computational Linguistics. 2022;7020-7030.
- Zhang D, Chen F, Chen X. DualGATs: dual graph attention networks for emotion recognition in conversations. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2023;1:7395-7408.
- Qin L, Li Z, Che W, Ni M, Liu T. Co-GAT: a co-interactive graph attention network for joint dialog act recognition and sentiment classification. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2021;35(15):13709- 13717.
- Li S, Chen X. Multiple Information-Aware Recurrent Reasoning Network for Joint Dialogue Act recognition and Sentiment Classification. Information. 2023;14(11):593. doi: 10.3390/info14110593
- Li Y, Su H, Shen X, Li W, Cao Z, Niu S. DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset. In: Proceedings of the Eighth International Joint Conference on Natural Language Processing. 2017;1:986–995.
- Schegloff EA. Sequence Organization in Interaction: A Primer in Conversation Analysis. Cambridge, UK: Cambridge University Press. 2007;1. doi: 10.1017/cbo9780511791208
- Pennebaker JW, King LA. Linguistic styles: language use as an individual difference. J Pers Soc Psychol. 1999;77(6):1296- 1312. doi: 10.1037/0022-3514.77.6.1296.
- Tett RP, Guterman HA. Situation trait relevance, trait expression, and cross-situational consistency: testing a principle of trait activation. J Res Pers. 2000;34(4):397-423. doi: 10.1006/jrpe.2000.2292.
- Searle JR. Speech Acts: An Essay in the Philosophy of Language. Cambridge, UK: Cambridge University Press; 1969.
- Hatfield E, Cacioppo JT, Rapson RL. Emotional contagion. Curr Dir Psychol Sci. 1993;2(3):96-100. doi: 10.1111/1467-8721.ep10770953.
- Paszke A, Gross S, Chintala S, et al. Automatic differentiation in PyTorch. In: Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017). 2017.
- Kazameini A, Fatehi S, Mehta Y, Eetemadi S, Cambria E. Personality trait detection using bagged SVM over BERT word embedding ensembles. arXiv 2020. https://arxiv.org/ abs/2010.01309