Design and Implementation of a Transformer-Based Emotionally Intelligent Chatbot

References

[1] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention Is All You Need,” Advances in Neural Information Processing Systems (NeurIPS), 2017.

[2] N. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” NAACL-HLT, 2019.

[3] J. Demszky et al., “GoEmotions: A Dataset of Fine-Grained Emotions,” Proceedings of ACL, 2020.

[4] S. Rashkin, E. M. Smith, M. Li, and Y.-L. Boureau, “Towards Empathetic Open-Domain Conversation Models,” ACL Proceedings, 2019.

[5] E. Cambria, S. Poria, R. Bajpai, and B. Schuller, “SenticNet 7: A Commonsense-Based AI Framework for Emotion Understanding,” AAAI Conference on Artificial Intelligence, 2023.

[6] D. Hazarika, S. Poria, R. Mihalcea, and E. Cambria, “Emotion Recognition and Sentiment Analysis in Conversational AI: A Multimodal Review,” IEEE Transactions on Affective Computing, vol. 13, no. 2, pp. 641–658, 2022.

[7] C. Hsu, C. Chen, and M. Chen, “EmotionLines: An Emotion Corpus of Multi-Party Conversations,” LREC, 2018.

[8] M. Buechel and U. Hahn, “EmoBank: A Corpus of Emotion Annotations in Texts,” LREC, 2017.

[9] S. Mohammad and P. Turney, “Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon,” NAACL-HLT, 2013.

[10] E. Cambria and A. Hussain,Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis, Springer, 2015.

[11] R. Mihalcea and C. Strapparava, “Learning to Laugh (Automatically): Computational Models for Humor Recognition,” Computational Linguistics, vol. 42, no. 1, pp. 71–98, 2016.

[12] P. Ekman, “An Argument for Basic Emotions,” Cognition and Emotion, vol. 6, no. 3–4, pp. 169–200, 1992.

[13] B. Liu, “Sentiment Analysis and Opinion Mining,” Synthesis Lectures on Human Language Technologies, vol. 5, no. 1, pp. 1–167, 2012.

[14] J. Bianchi and E. Hovy, “Towards Emotionally Aware AI Systems: Affective Reasoning and its Computational Modeling,” AI & Society, Springer, 2021.

[15] A. Colombo, E. Strapparava, and E. Cambria, “Affective Computing for Conversational Agents: Beyond Sentiment Analysis,” IEEE Computational Intelligence Magazine, vol. 17, no. 1, pp. 32–48, 2022.

[16] R. Cowie and M. Schröder, “Human Perception and Machine Analysis of Emotion in Speech,” Speech Communication, vol. 40, no. 1–2, pp. 1–27, 2003.

[17] A. Zadeh, M. Chen, S. Poria, E. Cambria, and L.-P. Morency, “Tensor Fusion Network for Multimodal Sentiment Analysis,” EMNLP, 2017.

[18] T. Wolf et al., “HuggingFace Transformers: State-of-the-Art Natural Language Processing,” EMNLP System Demonstrations, 2020.

[19] D. Silver et al., “Mastering the Game of Go with Deep Neural Networks and Tree Search,” Nature, vol. 529, pp. 484–489, 2016.

[20] A. Radford et al., “Language Models Are Unsupervised Multitask Learners,” OpenAI Technical Report, 2019.

[21] OpenAI Research, “Fine-Tuning GPT Models for Empathy-Aware Conversational AI,” Technical Report, 2023.

[22] S. Poria, E. Cambria, D. Hazarika, and N. Mazumder, “Context-Dependent Sentiment and Emotion Analysis in Conversational Systems,” Proceedings of COLING, 2020.

[23] J. Weizenbaum, “ELIZA—A Computer Program for the Study of Natural Language Communication Between Man and Machine,” Communications of the ACM, vol. 9, no. 1, pp. 36–45, 1966.

[24] R. Wallace, “The ALICE Chatbot: A Linguistic Approach to Artificial Intelligence,” AI Magazine, 2009.

[25] Y. Adi, E. Kermany, and Y. Belinkov, “Fine-Tuning Pre-Trained Language Models for Emotion Recognition,” ACL Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, 2021.

[26] C. Pelachaud, “Multimodal Emotion Expression for Virtual Humans,” Philosophical Transactions of the Royal Society B: Biological Sciences, vol. 364, pp. 3539–3548, 2009.

[27] A. Mehrabian, “Communication without Words,” Psychology Today, vol. 2, no. 4, pp. 53–56, 1968.

[28] D. Ghosh, S. Hasan, and V. Ng, “Emotion Detection from Text: A Survey,” SocialNLP Workshop, ACL, 2017.

[29] J. Cahn, “The Generation of Affect in Synthesized Speech,” Journal of the Acoustical Society of America, vol. 100, no. 4, pp. 2338–2350, 1996.

[30] J. Russell, “A Circumplex Model of Affect,” Journal of Personality and Social Psychology, vol. 39, no. 6, pp. 1161–1178, 1980.

[31] IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems,Ethically Aligned Design: A Vision for Prioritizing Human Well-being with Autonomous and Intelligent Systems, IEEE Standards Association, 2021.

[32] European Union,General Data Protection Regulation (GDPR), Regulation (EU) 2016/679, 2018.

[33] A. Batliner, B. Schuller, D. Seppi, and S. Steidl, “The Recognition of Emotions in Speech: A Review of Research and Applications,” IEEE Transactions on Affective Computing, vol. 1, no. 2, pp. 89–111, 2010.

[34] S. Prendinger and M. Ishizuka, “The Empathic Companion: A Character-Based Interface That Addresses Users’ Affective States,” Applied Artificial Intelligence, vol. 19, no. 3–4, pp. 267–285, 2005.

[35] A. McDuff, R. El Kaliouby, and R. Picard, “Crowdsourced Data Collection of Facial Responses to Online Videos,” Proceedings of the IEEE Conference on Automatic Face and Gesture Recognition, 2013.

@article{1711982,
      author = {Shweta Tarade, Tanvi Mokashie, Pranjali Yadav, Shreya Nikam, Prof. Prajwal Pawar},
      title = {Design and Implementation of a Transformer-Based Emotionally Intelligent Chatbot},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {5},
      pages = {798-824},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1711982.pdf},
      abstract = {The emergence of transformer-based architectures has revolutionized conversational artificial intelligence (AI) by enabling machines to understand and generate human-like dialogue. However, while modern chatbots demonstrate linguistic fluency, they often lack the emotional awareness and empathy necessary for effective human?AI communication. This paper presents a theoretical design and conceptual framework for a Transformer-Based Emotionally Intelligent Chatbot (TEIC) that integrates Natural Language Processing (NLP), Affective Computing, and Deep Learning techniques to recognize, interpret, and respond to human emotions in text-based conversations. The proposed model combines sentiment and emotion classification layers with a fine-tuned transformer backbone (based on GPT/BERT family architectures) to achieve contextually and emotionally coherent dialogue. The system architecture is designed to simulate emotional intelligence (EI) through three key modules: emotion perception, emotion reasoning, and emotion expression. The study also explores the ethical, computational, and linguistic challenges of embedding emotion understanding within AI models. Experimental references and comparative studies demonstrate that integrating affective features enhances user engagement, satisfaction, and trust compared to traditional chatbots. This research contributes to the development of empathetic conversational agents capable of bridging the cognitive?emotional gap in human?machine interaction.},
      keywords = {Artificial Intelligence (AI), Chatbot, Emotion Detection, Transformer Architecture, Natural Language Processing (NLP), Affective Computing, Deep Learning, Generative AI, Emotional Intelligence (EI), Conversational Systems.},
      month = {November},
      doi = {https://doi.org/10.64388/IREV9I5-1711982}
  }