{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T15:59:55Z","timestamp":1784217595749,"version":"3.55.0"},"reference-count":60,"publisher":"Association for Computing Machinery (ACM)","issue":"8","license":[{"start":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T00:00:00Z","timestamp":1723075200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2024,8,31]]},"abstract":"<jats:p>Current industry trends demand automation in every aspect, where machines could replace humans. Recent advancements in conversational agents have grabbed a lot of attention from industries, markets, and businesses. Building conversational agents that exhibit human communication characteristics is a need in today's marketplace. Thus, by accumulating emotions, we can build emotionally aware conversational agents. Emotion detection in text-based dialogues has turned into a pivotal component of conversational agents, enhancing their ability to understand and respond to users\u2019 emotional states. This article extensively compares various artificial intelligence techniques adapted to text-based emotion detection for conversational agents. The study covers a wide range of methods, from machine learning models to cutting-edge pre-trained models and deep learning models. We evaluate the performance of these techniques on the benchmark unbalanced Topical-Chat and balanced Empathetic Dialogue datasets. This article offers an overview of the practical implications of emotion detection techniques in conversational systems and their impact on user response. The outcomes of this work contribute to the ongoing development of empathetic conversational agents, emphasizing natural human-machine interactions.<\/jats:p>","DOI":"10.1145\/3643133","type":"journal-article","created":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T11:58:57Z","timestamp":1706702337000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Understanding the Performance of AI Algorithms in Text-Based Emotion Detection for Conversational Agents"],"prefix":"10.1145","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9830-6619","authenticated-orcid":false,"given":"Sheetal D.","family":"Kusal","sequence":"first","affiliation":[{"name":"Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4903-1540","authenticated-orcid":false,"given":"Shruti G.","family":"Patil","sequence":"additional","affiliation":[{"name":"Symbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0881-5477","authenticated-orcid":false,"given":"Jyoti","family":"Choudrie","sequence":"additional","affiliation":[{"name":"University of Hertfordshire, Hatfield, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2653-3780","authenticated-orcid":false,"given":"Ketan V.","family":"Kotecha","sequence":"additional","affiliation":[{"name":"Symbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,8,8]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2020.100239"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICEBE.2018.00019"},{"key":"e_1_3_1_4_2","first-page":"301","volume-title":"Proceedings of the 14th International Conference on Wirtschaftsinformatik","author":"Hobert S.","year":"2019","unstructured":"S. Hobert and R. Meyer von Wolff. 2019. Say hello to your new automated tutor\u2014A structured literature review on pedagogical conversational agents. In Proceedings of the 14th International Conference on Wirtschaftsinformatik. 301\u2013314."},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3267851.3267896"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.03.054"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.5555\/265013"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1080\/08839510590910174"},{"key":"e_1_3_1_9_2","volume-title":"Proceedings of the 8th International Conference on Spoken Language Processing (INTERSPEECH \u201904)","author":"Tao J.","year":"2004","unstructured":"J. Tao. 2004. Context based emotion detection from text input. In Proceedings of the 8th International Conference on Spoken Language Processing (INTERSPEECH \u201904)."},{"key":"e_1_3_1_10_2","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/978-3-319-19581-0_17","volume-title":"Natural Language Processing and Information Systems","volume":"9013","author":"Udochukwu O.","year":"2015","unstructured":"O. Udochukwu and Y. He. 2015. A rule-based approach to implicit emotion detection in text. In Natural Language Processing and Information Systems. Lecture Notes in Computer Science, Vol. 9013. Springer, 197\u2013203."},{"key":"e_1_3_1_11_2","volume-title":"2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE)","year":"2018","unstructured":"IEEE. 2018. 2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE). IEEE."},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.08.005"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1002\/eng2.12189"},{"key":"e_1_3_1_14_2","unstructured":"M. Mnasri. 2019. Recent advances in conversational NLP: Towards the standardization of Chatbot building. arXiv:1903.09025 (2019). http:\/\/arxiv.org\/abs\/1903.09025"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(71)90002-6"},{"key":"e_1_3_1_16_2","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/978-3-642-12397-9_14","volume-title":"Development of Multimodal Interfaces: Active Listening and Synchrony","volume":"5967","author":"Skowron M.","year":"2010","unstructured":"M. Skowron. 2010. Affect listeners: Acquisition of affective states by means of conversational systems. In Development of Multimodal Interfaces: Active Listening and Synchrony. Lecture Notes in Computer Science, Vol. 5967. Springer, 169\u2013181."},{"issue":"1","key":"e_1_3_1_17_2","first-page":"118","article-title":"Sentiment analysis and classification using lexicon-based approach and addressing polarity shift problem","volume":"90","author":"v Kolekar N.","year":"2016","unstructured":"N. v Kolekar, P. Gauri Rao, S. Dey, M. Mane, V. Jadhav, and S. Patil. 2016. Sentiment analysis and classification using lexicon-based approach and addressing polarity shift problem. Journal of Theoretical and Applied Information Technology 90, 1 (2016), 118\u2013125.","journal-title":"Journal of Theoretical and Applied Information Technology"},{"key":"e_1_3_1_18_2","doi-asserted-by":"crossref","first-page":"330","DOI":"10.18653\/v1\/S19-2057","volume-title":"Proceedings of the 13th International Workshop on Semantic Evaluation","author":"Basile A.","year":"2019","unstructured":"A. Basile, M. Franco-Salvador, N. Pawar, S. Sanja\u0161tajner, M. C. Rios, and Y. Benajiba. 2019. SymantoResearch at SemEval-2019 Task 3: Combined neural models for emotion classification in human-chatbot conversations. In Proceedings of the 13th International Workshop on Semantic Evaluation. 330\u2013334."},{"key":"e_1_3_1_19_2","volume-title":"Proceedings of the 2019 IEEE 17th International Conference on Industrial Informatics (INDIN \u201919)","author":"Adikari A.","year":"2019","unstructured":"A. Adikari, D. de Silva, D. Alahakoon, and X. Yu. 2019. A cognitive model for emotion awareness in industrial chatbots. In Proceedings of the 2019 IEEE 17th International Conference on Industrial Informatics (INDIN \u201919)."},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.5220\/0007771001000107"},{"key":"e_1_3_1_21_2","doi-asserted-by":"crossref","first-page":"133","DOI":"10.18653\/v1\/2021.conll-1.10","volume-title":"Proceedings of the 25th Conference on Computational Natural Language Learning (CoNLL \u201921)","author":"Xie Y.","year":"2021","unstructured":"Y. Xie and P. Pu. 2021. Empathetic dialog generation with fine-grained intents. In Proceedings of the 25th Conference on Computational Natural Language Learning (CoNLL \u201921). 133\u2013147. http:\/\/arxiv.org\/abs\/2105.06829"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1051\/smdo\/2020019"},{"key":"e_1_3_1_23_2","first-page":"49","volume-title":"Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)","author":"Huang C.","year":"2018","unstructured":"C. Huang, O. R. Za\u0131ane, A. Trabelsi, and N. Dziri. 2018. Automatic dialogue generation with expressed emotions. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers). 49\u201354. http:\/\/www.cs.ualberta.ca\/"},{"key":"e_1_3_1_24_2","first-page":"730","volume-title":"Proceedings of the 32nd AAAI Conference on Artificial Intelligence, the 30th Innovative Applications of Artificial Intelligence Conference, and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (AAAI\/IAAI\/EAAI \u201918)","author":"Zhou H.","year":"2018","unstructured":"H. Zhou, M. Huang, T. Zhang, X. Zhu, and B. Liu. 2018. Emotional chatting machine: Emotional conversation generation with internal and external memory. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence, the 30th Innovative Applications of Artificial Intelligence Conference, and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (AAAI\/IAAI\/EAAI \u201918). 730\u2013738. http:\/\/arxiv.org\/abs\/1704.01074"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210002"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN52387.2021.9533452"},{"key":"e_1_3_1_27_2","doi-asserted-by":"crossref","first-page":"162","DOI":"10.18653\/v1\/S18-1023","volume-title":"Proceedings of the 12th International Workshop on Semantic Evaluation","author":"Ezen-Can A.","year":"2018","unstructured":"A. Ezen-Can and E. F. Can. 2018. RNN for Affects at SemEval-2018 Task 1: Formulating affect identification as a binary classification problem. In Proceedings of the 12th International Workshop on Semantic Evaluation. 162\u2013166."},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2022.06.072"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2022.3149234"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICACCI.2016.7732476"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107861"},{"key":"e_1_3_1_32_2","doi-asserted-by":"crossref","first-page":"123","DOI":"10.18653\/v1\/S18-1016","volume-title":"Proceedings of the 12th International Workshop on Semantic Evaluation","author":"De Bruyne L.","year":"2018","unstructured":"L. De Bruyne, O. De Clercq, and V. Hoste. 2018. LT3 at SemEval-2018 Task 1: A classifier chain to detect emotions in tweets. In Proceedings of the 12th International Workshop on Semantic Evaluation. 123\u2013127."},{"key":"e_1_3_1_33_2","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1007\/978-981-15-1097-7_47","volume-title":"Data Engineering and Communication Technology","author":"Suhasini M.","year":"2020","unstructured":"M. Suhasini and B. Srinivasu. 2020. Emotion detection framework for Twitter data using supervised classifers. In Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, Vol. 1079. Springer, 565\u2013576."},{"key":"e_1_3_1_34_2","volume-title":"Proceedings of the 2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE \u201918)","author":"Allouch Merav","year":"2018","unstructured":"Merav Allouch, Amos Azaria, Rina Azoulay, Ester Ben-Izchak, and Moti Zwilling. 2018. Automatic detection of insulting sentences in conversation. In Proceedings of the 2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE \u201918)."},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1108\/JHTI-03-2023-0166"},{"key":"e_1_3_1_36_2","volume-title":"Proceedings of the 13th International Workshop on Semantic Evaluation","author":"Basile A.","year":"2019","unstructured":"A. Basile, M. Franco-Salvador, N. Pawar, S. Sanja\u0161tajner, M. C. Rios, and Y. Benajiba. 2019. SymantoResearch at SemEval-2019 Task 3: Combined neural models for emotion classification in human-chatbot conversations. In Proceedings of the 13th International Workshop on Semantic Evaluation."},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-019-07813-9"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/3463677.3463682"},{"key":"e_1_3_1_39_2","volume-title":"Proceedings of the 13th International Workshop on Semantic Evaluation","author":"Xiao J.","year":"2019","unstructured":"J. Xiao. 2019. Figure Eight at SemEval-2019 Task 3: Ensemble of transfer learning methods for contextual emotion detection. In Proceedings of the 13th International Workshop on Semantic Evaluation. https:\/\/github.com\/fastai\/fastai"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2020.102262"},{"key":"e_1_3_1_41_2","doi-asserted-by":"crossref","unstructured":"D. Cortiz. 2021. Exploring Transformers in emotion recognition: A comparison of BERT DistilBERT RoBERTa XLNet and ELECTRA. arXiv:2104.02041 (2021).","DOI":"10.1145\/3562007.3562051"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.3390\/bdcc5030043"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3201144"},{"key":"e_1_3_1_44_2","volume-title":"Proceedings of the 8th International Conference on Spoken Language Processing (INTERSPEECH \u201919)","author":"Gopalakrishnan K.","year":"2019","unstructured":"K. Gopalakrishnan, B. Hedayatnia, Q. Chen, A. Gottardi, S. Kwarta, A. Venkatesh, R. Gabriel, and D. Hakkani-Tur. 2019. Topical-Chat: Towards knowledge-grounded open-domain conversations. In Proceedings of the 8th International Conference on Spoken Language Processing (INTERSPEECH \u201919)."},{"key":"e_1_3_1_45_2","doi-asserted-by":"crossref","unstructured":"H. Rashkin E. M. Smith M. Li and Y.-L. Boureau. 2018. Towards empathetic open-domain conversation models: A new benchmark and dataset. arXiv:1811.00207 (2018). http:\/\/arxiv.org\/abs\/1811.00207","DOI":"10.18653\/v1\/P19-1534"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-021-10039-7"},{"key":"e_1_3_1_47_2","first-page":"531","volume-title":"Proceedings of the 2nd International Conference on Communication, Computing and Networking","volume":"46","author":"Singh Lovejit","year":"2018","unstructured":"Lovejit Singh, Sarbjeet Singh, and Naveen Aggarwal. 2018. Two-stage text feature selection method for human emotion recognition. In Proceedings of the 2nd International Conference on Communication, Computing and Networking. Lecture Notes in Networks and Systems, Vol. 46. Springer, 531\u2013538."},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-74628-7_27"},{"key":"e_1_3_1_49_2","doi-asserted-by":"crossref","first-page":"747","DOI":"10.18653\/v1\/S17-2126","volume-title":"Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval \u201917)","author":"Baziotis C.","year":"2017","unstructured":"C. Baziotis, N. Pelekis, and C. Doulkeridis. 2017. DataStories at SemEval-2017 Task 4: Deep LSTM with attention for message-level and topic-based sentiment analysis. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval \u201917). 747\u2013754."},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/T-AFFC.2011.33"},{"issue":"9","key":"e_1_3_1_51_2","doi-asserted-by":"crossref","first-page":"4332","DOI":"10.1109\/TNNLS.2021.3056664","article-title":"Attention-emotion-enhanced convolutional LSTM for sentiment analysis","volume":"33","author":"Huang F.","year":"2021","unstructured":"F. Huang, X. Li, C. Yuan, S. Zhang, J. Zhang, and S. Qiao. 2021. Attention-emotion-enhanced convolutional LSTM for sentiment analysis. IEEE Transactions on Neural Networks and Learning Systems 33, 9 (2021), 4332\u20134345.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112851"},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-019-01485-x"},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-020-00803-0"},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-021-09958-2"},{"key":"e_1_3_1_56_2","doi-asserted-by":"crossref","first-page":"49","DOI":"10.18653\/v1\/S19-2006","volume-title":"Proceedings of the 13th International Workshop on Semantic Evaluation","author":"Huang C.","year":"2019","unstructured":"C. Huang, A. Trabelsi, and O. R. Za\u00efane. 2019. ANA at SemEval-2019 Task 3: Contextual emotion detection in conversations through hierarchical LSTMs and BERT. In Proceedings of the 13th International Workshop on Semantic Evaluation. 49\u201353. http:\/\/arxiv.org\/abs\/1904.00132"},{"key":"e_1_3_1_57_2","unstructured":"Y.-H. Huang S.-R. Lee M.-Y. Ma Y.-H. Chen Y.-W. Yu and Y.-S. Chen. 2019. EmotionX-IDEA: Emotion BERT\u2014An affectional model for conversation. arXiv:1908.06264 (2019). http:\/\/nlp.mathcs.emory.edu"},{"key":"e_1_3_1_58_2","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/B978-0-08-100508-8.00009-6","article-title":"Sentiment analysis: Detecting valence, emotions, and other affectual states from text","volume":"2016","author":"Mohammad S. M.","year":"2016","unstructured":"S. M. Mohammad. 2016. Sentiment analysis: Detecting valence, emotions, and other affectual states from text. Emotion Measurement 2016 (2016), 201\u2013237.","journal-title":"Emotion Measurement"},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2016.10.004"},{"key":"e_1_3_1_60_2","doi-asserted-by":"crossref","first-page":"5327","DOI":"10.1007\/s12652-019-01419-7","article-title":"Empirical study of shallow and deep learning models for sarcasm detection using context in benchmark datasets","volume":"14","author":"Kumar A.","year":"2019","unstructured":"A. Kumar and G. Garg. 2019. Empirical study of shallow and deep learning models for sarcasm detection using context in benchmark datasets. Journal of Ambient Intelligence and Humanized Computing 14 (2019), 5327\u20135342.","journal-title":"Journal of Ambient Intelligence and Humanized Computing"},{"key":"e_1_3_1_61_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2019.04.006"}],"container-title":["ACM Transactions on Asian and Low-Resource Language Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3643133","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3643133","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T16:31:21Z","timestamp":1750264281000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3643133"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,8]]},"references-count":60,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,8,31]]}},"alternative-id":["10.1145\/3643133"],"URL":"https:\/\/doi.org\/10.1145\/3643133","relation":{},"ISSN":["2375-4699","2375-4702"],"issn-type":[{"value":"2375-4699","type":"print"},{"value":"2375-4702","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,8]]},"assertion":[{"value":"2023-01-19","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-01-03","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-08-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}