Skip to main content
SpringerLink
Log in

A survey on emotion–cause extraction in psychological text using deep learning methods

  • Review
  • Published:
Progress in Artificial Intelligence Aims and scope Submit manuscript

Abstract

The study that has been done in this survey paper mainly focuses on the recent work in detecting emotions based on causes. The emotion–cause extraction approach is discussed in depth, and many variants are compared to achieve a contrasting result. The use of causes when extracting emotions in a sentence requires following the casual relationship at each process stage. The O(n2) drawback of the emotion–cause pair extraction approach is contrasted with the transition approach of following the casual relationship of emotion and causes. A new approach that works on the emotion–cause pair extraction technique uses the attention, and transition methods are discussed in great depth by introducing all the related technologies. Enhanced deep learning methods that can easily replace the current state-of-the-art models by fine-tuning pretrained models like bidirectional encoder representations from transformers are studied, and results are compared on various use cases. These models are helping in the emergence of hierarchical networks for the optimization of preexisting methods. The psychological dataset is used to compare various techniques. The dataset requires annotation with the emotion clauses and cause clauses to train the models. This survey has reviewed many papers, and the advantages and disadvantages of the approaches introduced in these papers have been discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Yarkoni, T., Westfall, J.: Choosing prediction over explanation in psychology: lessons from machine learning. Perspect. Psychol. Sci. 12(6), 1100–1122 (2017)

    Article  Google Scholar 

  2. Hagerty, M.R., Srinivasan, V.: Comparing the predictive powers of alternative multiple regression models. Psychometrika 56(1), 77–85 (1991)

    Article  MathSciNet  Google Scholar 

  3. Shmueli, G.: To explain or to predict? Stat. Sci. 25(3), 289–310 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  4. Wu, S., Harris, T.J., McAuley, K.B.: The use of simplified or misspecified models: linear case. Canad. J. Chem. Eng. 85(4), 386–398 (2007)

    Article  Google Scholar 

  5. Lee, S.Y.M., Chen, Y., Huang, C.R.: A text-driven rule-based system for emotion cause detection. In: Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 45–53 (2010)

  6. Malcangi, M.: Text-driven avatars based on artificial neural networks and fuzzy logic. Int. J. Comput. 4(2), 61–69 (2010)

    Google Scholar 

  7. Li, W., Xu, H.: Text-based emotion classification using emotion cause extraction. Expert Syst. Appl. 41(4), 1742–1749 (2014)

    Article  Google Scholar 

  8. Spanoudakis, G., Zisman, A., Pérez-Minana, E., Krause, P.: Rule-based generation of requirements traceability relations. J. Syst. Softw. 72(2), 105–127 (2004)

    Article  Google Scholar 

  9. Lee, Y.M.S.: A linguistic approach to emotion detection and classification (2010)

  10. Wierzbicka, A.: Defining emotion concepts. Cogn. Sci. 16(4), 539–581 (1992)

    Article  Google Scholar 

  11. Pustejovsky, J.: The Generative Lexicon. MIT press, New York (1998)

    Book  Google Scholar 

  12. Chen, Y., Lee, S.Y.M., Li, S., Huang, C.R.: Emotion cause detection with linguistic constructions. In: Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010), pp. 179–187 (2010)

  13. Mihalcea, R., Liu, H.: A corpus-based approach to finding happiness. In: AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs, pp 139–144 (2010)

  14. Persing, I., Ng, V.: Semi-supervised cause identification from aviation safety reports. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, pp 843–851 (2009)

  15. Marcu, D., Echihabi, A.: An unsupervised approach to recognizing discourse relations. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 368–375 (2002)

  16. Girju, R.: Automatic detection of causal relations for question answering. In: Proceedings of the ACL 2003 Workshop on Multilingual Summarization and Question Answering, pp. 76–83 (2003)

  17. Wei, P., Zhao, J., Mao, W.: Effective inter-clause modeling for end-to-end emotion-cause pair extraction. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3171–3181 (2020)

  18. Xu, B., Lin, H., Lin, Y., Diao, Y., Yang, L., Xu, K.: Extracting emotion causes using learning to rank methods from an information retrieval perspective. IEEE Access 7, 15573–15583 (2019)

    Article  Google Scholar 

  19. Ding, Z., He, H., Zhang, M., Xia, R.: From independent prediction to reordered prediction: Integrating relative position and global label information to emotion cause identification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, No. 01, pp. 6343–6350 (2019)

  20. Gui, L., Hu, J., He, Y., Xu, R., Lu, Q., Du, J.: A question answering approach to emotion cause extraction. arXiv:1708.05482 (2017)

  21. Gao, W., Li, S., Lee, S.Y.M., Zhou, G., Huang, C.R.: Joint learning on sentiment and emotion classification. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, pp. 1505–1508 (2013)

  22. Das, D., Bandyopadhyay, S.: Finding emotion holder from Bengali blog texts—an unsupervised syntactic approach. In: Proceedings of the 24th Pacific Asia Conference on Language, Information and Computation, pp. 621–628 (2010)

  23. Ou, G., Chen, W., Wang, T., Wei, Z., Li, B., Yang, D., Wong, K.F.: Exploiting community emotion for microblog event detection. In: Social Media Content Analysis: Natural Language Processing and Beyond, pp. 439–456 (2018)

  24. Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. arXiv:1605.08900 (2016)

  25. Chen, Y., Hou, W., Cheng, X., Li, S.: Joint learning for emotion classification and emotion cause detection. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 646–651 (2018)

  26. Huang, L., Li, S., Zhou, G.: Emotion corpus construction on microblog text. In: Workshop on Chinese Lexical Semantics, pp. 204–212. Springer, Cham (2015)

  27. Kumar, A., Jain, A.K.: Emotion detection in psychological texts by fine-tuning BERT using emotion–cause pair extraction. Int. J. Speech Technol. 25(3), 727–743 (2022)

    Article  Google Scholar 

  28. Wang, R., Li, S., Zhou, G., Shi, H.: Joint sentiment and emotion classification with integer linear programming. In: International Conference on Database Systems for Advanced Applications, pp. 259–265. Springer, Cham (2015)

  29. Cheng, X., Chen, Y., Cheng, B., Li, S., Zhou, G.: An emotion cause corpus for Chinese microblogs with multiple-user structures. ACM Trans. Asian Low Resour. Lang. Inf. Process. TALLIP 17(1), 1–19 (2017)

    Google Scholar 

  30. Hu, G., Lu, G., Zhao, Y.: Emotion-cause joint detection: a unified network with dual interaction for emotion cause analysis. In: CCF International Conference on Natural Language Processing and Chinese Computing, pp. 568–579. Springer, Cham (2020)

  31. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv:1409.0473 (2014)

  32. Li, X., Song, K., Feng, S., Wang, D., Zhang, Y.: A co-attention neural network model for emotion cause analysis with emotional context-awareness. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 4752–4757 (2014)

  33. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)

  34. Xu, R., Hu, J., Lu, Q., Wu, D., Gui, L.: An ensemble approach for emotion cause detection with event extraction and multi-kernel SVM. Tsinghua Sci. Technol. 22(6), 646–659 (2017)

    Article  Google Scholar 

  35. Wu, F., Duan, X., Xiao, J., Zhao, Z., Tang, S., Zhang, Y., Zhuang, Y.: Temporal interaction and causal influence in community-based question answering. IEEE Trans. Knowl. Data Eng. 29(10), 2304–2317 (2017)

    Article  Google Scholar 

  36. Yu, X., Rong, W., Zhang, Z., Ouyang, Y., Xiong, Z.: Multiple level hierarchical network-based clause selection for emotion cause extraction. IEEE Access 7, 9071–9079 (2019)

    Article  Google Scholar 

  37. Zhang, Y., Zhong, V., Chen, D., Angeli, G., Manning, C.D.: Position-aware attention and supervised data improve slot filling. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 35–45 (2017)

  38. Shen, T., Zhou, T., Long, G., Jiang, J., Pan, S., Zhang, C.: Disan: Directional self-attention network for rnn/CNN-free language understanding. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, No. 1 (2018)

  39. Cho, K., Van Merriënboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder–decoder approaches. arXiv:1409.1259 (2014)

  40. Xia, R., Zhang, M., Ding, Z.: RTN: a rnn-transformer hierarchical network for emotion cause extraction. arXiv:1906.01236. (2019)

  41. Neviarouskaya, A., Aono, M.: Extracting causes of emotions from text. In: Proceedings of the Sixth International Joint Conference on Natural Language Processing, pp. 932–936 (2013)

  42. Yada, S., Ikeda, K., Hoashi, K., Kageura, K.: A bootstrap method for automatic rule acquisition on emotion cause extraction. In: IEEE International Conference on Data Mining Workshops, pp. 414–421 (2017).

  43. Li, X., Feng, S., Wang, D., Zhang, Y.: Context-aware emotion cause analysis with multi-attention-based neural network. Knowl. Based Syst. 174, 205–218 (2019)

    Article  Google Scholar 

  44. Gabarron, E., Larbi, D., Denecke, K., Årsand, E.: What do we know about the use of chatbots for public health?. In: Digital Personalized Health and Medicine, pp. 796–800 (2020)

  45. Kjell, O.N., Kjell, K., Garcia, D., Sikström, S.: Semantic measures: Using natural language processing to measure, differentiate, and describe psychological constructs. Psychol. Methods 24(1), 92 (2019)

    Article  Google Scholar 

  46. Creer, S.D., McCarthy, K.S., Magliano, J.P., McNamara, D.S., Allen, L.K.: Self-Explanation vs. Think Aloud: What Natural Language Processing Can Tell Us (No. 3624). EasyChair (2020)

  47. Misyak, J.B., Christiansen, M.H., Tomblin, J.B.: On-line individual differences in statistical learning predict language processing. Front. Psychol. 1, 31 (2010)

    Article  Google Scholar 

  48. El Sandy, W.S.M., El Sayed Dadour, D., Kouta, A.: A suggested NLP-based framework for teaching writing to adhd pupils. Res. J. English Lang. Lit. 4(1) (2016)

  49. Divya, S., Indumathi, V., Ishwarya, S., Priyasankari, M., Devi, S.K.: A self-diagnosis medical chatbot using artificial intelligence. J. Web Dev. Web Design. 3(1), 1–7 (2018)

    Google Scholar 

  50. Oh, K.J., Lee, D., Ko, B., Choi, H.J.: A chatbot for psychiatric counseling in mental healthcare service based on emotional dialogue analysis and sentence generation. In: 2017 18th IEEE International Conference on Mobile Data Management (MDM), pp. 371–375. IEEE (2017)

  51. Vaidyam, A.N., Wisniewski, H., Halamka, J.D., Kashavan, M.S., Torous, J.B.: Chatbots and conversational agents in mental health: a review of the psychiatric landscape. Canad. J. Psychiatry 64(7), 456–464 (2019)

    Article  Google Scholar 

  52. Stiefel, S.: The chatbot will see you now: protecting mental health confidentiality in software applications. Colum. Sci. Technol. Law Rev. 20, 333 (2018)

    Google Scholar 

  53. Beiley, M.R.: Mental Health and Wellness Chatbot (2019)

  54. Kamita, T., Ito, T., Matsumoto, A., Munakata, T., Inoue, T.: A chatbot system for mental healthcare based on SAT counseling method. Mobile Inf. Syst. 2019 (2019)

  55. Lee, M., Ackermans, S., Van As, N., Chang, H., Lucas, E., IJsselsteijn, W.: Caring for Vincent: a chatbot for self-compassion. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1–13 (2019)

  56. Delahunty, F., Arcan, M., Johansson, R.: Passive Diagnosis of Mental Health Disorders Incorporating an Empathic Dialogue System (2019)

  57. Mehrjardi, M.S., Trabelsi, A., Zaiane, O.R.: Self-Attentional Models Application in Task-Oriented Dialogue Generation Systems. arXiv:1909.05246 (2019)

  58. Ralston, K., Chen, Y., Isah, H., Zulkernine, F.: A voice interactive multilingual student support system using IBM Watson. In: 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), pp. 1924–1929. IEEE (2019)

  59. Patel, F., Thakore, R., Nandwani, I., Bharti, S.K.: Combating depression in students using an intelligent chatBot: a cognitive behavioral therapy. In: 2019 IEEE 16th India Council International Conference (INDICON), pp. 1–4. IEEE (2019)

  60. Sharma, B., Puri, H., Rawat, D.: Digital psychiatry-curbing depression using therapy chatbot and depression analysis. In: 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 627–631. IEEE (2018)

  61. Kretzschmar, K., Tyrol, H., Pavarini, G., Manzini, A., Singh, I., NeurOx Young People's Advisory Group.: Can your phone be your therapist? Young people's ethical perspectives on the use of fully automated conversational agents (chatbots) in mental health support. Biomed. Inf. Insights 11, 1178222619829083 (2019)

  62. Bell, S., Wood, C., Sarkar, A.: Perceptions of chatbots in therapy. In: Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1–6 (2019)

  63. Coutanche M.N., Hallion, L.S.: Machine learning for clinical psychology and clinical neuroscience (2019)

  64. Fowler, B.M.: Stealing thunder and filling the silence: twitter as a primary channel of police crisis communication. Pub. Relat. Rev. 43(4), 718–728 (2017)

    Article  Google Scholar 

  65. Yu, J., Liu, W., He, Y., Zhang, C.: A mutually auxiliary multitask model with self-distillation for emotion-cause pair extraction. IEEE Access 9, 26811–26821 (2021)

    Article  Google Scholar 

  66. Barnaghi, P., Ghaffari, P., Breslin, J.G.: Opinion mining and sentiment polarity on twitter and correlation between events and sentiment. In: Proceedings of the IEEE 2nd International Conference on Big Data Computing Service Applications (BigDataService), pp. 52–57 (2016)

  67. Jones, N.M., Wojcik, S.P., Sweeting, J., Silver, R.C.: Tweeting negative emotion: an investigation of Twitter data in the aftermath of violence on college campuses. Psychol. Methods 21(4), 526–541 (2016)

    Article  Google Scholar 

  68. Buntain, C., Golbeck, J., Liu, B., LaFree, G.: Evaluating public response to the Boston Marathon bombing and other acts of terrorism through twitter. In: Proceedings of the International AAAI Conference on Web Social Media (ICWSM), pp. 555–558 (2016)

  69. Wang, X., Gerber, M.S., Brown, D.E.: Automatic crime prediction using events extracted from twitter posts. In: Proceedings of the International Conference on Social Computing Behavioral-Cultural Modeling Prediction, pp. 231–238 (2012)

  70. Hu, G., Lu, G., Zhao, Y.: FSS-GCN: a graph convolutional networks with fusion of semantic and structure for emotion cause analysis. Knowl. Based Syst. 212, 106584 (2021)

    Article  Google Scholar 

  71. Yan, H., Gui, L., Pergola, G., & He, Y.: Position Bias Mitigation: A Knowledge-Aware Graph Model for EmotionCause Extraction. arXiv:2106.03518 (2021)

  72. Xia, R., Ding, Z.: Emotion-cause pair extraction: a new task to emotion analysis in texts. arXiv:1906.01267 (2019)

  73. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018)

  74. Sun, C., Qiu, X., Xu, Y., Huang, X.: How to fine-tune bert for text classification?. In: China National Conference on Chinese Computational Linguistics, pp. 194–206. Springer, Cham (2019)

  75. Lee, S.Y.M., Chen, Y., Huang, C.R., Li, S.: Detecting emotion causes with a linguistic rule-based approach 1. Comput. Intell. 29(3), 390–416 (2013)

    Article  MathSciNet  Google Scholar 

  76. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)

  77. Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., et al: Roberta: a robustly optimized bert pre-training approach. arXiv:1907.11692 (2019)

  78. Sanh, V., Debut, L., Chaumond, J., Wolf, T.: Dilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv:1910.01108 (2019)

  79. Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R.R., & Le, Q.V.: Client: generalized autoregressive pre-training for language understanding. In: Advances in Neural Information Processing Systems, 32 (2019)

Download references

Author information

Authors and Affiliations

  1. National Institute of Technology Kurukshetra, Kurukshetra, India

    Anshul Kumar & Ankit Kumar Jain

Authors
  1. Anshul Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ankit Kumar Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ankit Kumar Jain.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, A., Jain, A.K. A survey on emotion–cause extraction in psychological text using deep learning methods. Prog Artif Intell 12, 303–321 (2023). https://doi.org/10.1007/s13748-023-00305-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13748-023-00305-w

Keywords

Search

Navigation