Emotion-Aware Question Answering for Intelligent Customer Service Systems
Keywords:
emotion-aware question answering; intelligent customer service; affective computing; natural language processing; system architecture; fairness; algorithmic governanceAbstract
Intelligent customer service platforms increasingly rely on question answering systems to interpret user requests, retrieve relevant knowledge, and generate responses. However, the effectiveness of these systems depends not only on factual accuracy but also on their ability to recognize and respond appropriately to the emotional states of customers. Emotion-aware question answering extends conventional natural language processing by integrating affective signals into the representation, retrieval, and generation pipeline. This paper presents a system-level analysis of emotion-aware question answering for customer service environments. It examines architectural trade-offs between modular emotion classifiers and end-to-end affective language models, the construction and governance of emotion-sensitive data infrastructure, and the operational challenges of deploying such systems at scale. The discussion emphasizes infrastructure design, robustness, fairness, interpretability, and policy compliance rather than isolated model performance. It considers how emotion recognition interacts with retrieval-augmented generation, contrastive representation learning, and conversational context modeling. The paper further addresses systemic risks, including demographic bias in emotion inference, privacy concerns arising from affective data collection, and the need for transparent model reporting. By synthesizing perspectives from natural language processing, affective computing, human-computer interaction, and algorithmic governance, the paper offers a forward-looking research agenda for building emotion-aware customer service systems that are accurate, equitable, and sustainable.
References
1. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 4171–4186.
2. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998–6008.
3. Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692.
4. Picard, R. W. (1997). Affective computing. MIT Press.
5. Poria, S., Hazarika, D., Majumder, N., & Mihalcea, R. (2019). Multimodal sentiment analysis: Addressing key issues and setting up the baselines. IEEE Transactions on Affective Computing, 10(4), 595–609.
6. Kratzwald, B., & Feuerriegel, S. (2019). Putting question answering on the map: A survey of recent approaches. IEEE Transactions on Knowledge and Data Engineering, 31(1), 1–20.
7. Chen, D., Fisch, A., Weston, J., & Bordes, A. (2017). Reading Wikipedia to answer open-domain questions. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 1870–1879.
8. Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., & Le, Q. V. (2019). XLNet: Generalized autoregressive pretraining for language understanding. Advances in Neural Information Processing Systems, 32, 5753–5763.
9. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems, 33, 9459–9474.
10. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623.
11. Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of Machine Learning Research, 81, 77–91.
12. Saleiro, P., Kuester, B., Hinkson, L., London, J., Stevens, A., Anisfeld, A., Rodolfa, K. T., & Ghani, R. (2019). Aequitas: A bias and fairness audit toolkit. arXiv preprint arXiv:1811.05577.
13. Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and machine learning. fairmlbook.org.
14. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Why should I trust you? Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144.
15. Li, Q. (2026). Dynamic Adaptive Attention and Supervised Contrastive Learning: A Novel Hybrid Framework for Text Sentiment Classification. arXiv preprint arXiv:2604.10459.
16. Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016). SQuAD: 100,000+ questions for machine comprehension of text. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2383–2392.
17. Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., & Choi, Y. (2019). HellaSwag: Can a machine really finish your sentence? Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 4791–4800.
18. Mehrabian, A. (1980). Basic dimensions for a general psychological theory: Implications for personality, social, environmental, and developmental studies. Oelgeschlager, Gunn & Hain.
19. Lazarus, R. S. (1991). Emotion and adaptation. Oxford University Press.
20. Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86–92.
21. Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model cards for model reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency, 220–229.
22. Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399.
23. Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., & Le, Q. V. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35, 24824–24837.
24. OpenAI. (2023). GPT-4 technical report. arXiv preprint arXiv:2303.08774.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Advanced Artificial Intelligence Research
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
