EXTRACTING EMOTION-CAUSE PAIRS: A BILSTM-DRIVEN METHODOLOGY

Raga Madhuri Chandra

chragamadhuri@vrsiddhartha.ac.in
Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering (India)
https://orcid.org/0000-0002-3687-0783

Giri Venkata Sai Tej Neelaiahgari


Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering (India)
https://orcid.org/0009-0009-5785-0413

Satya Sumanth Vanapalli


Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering (India)
https://orcid.org/0009-0006-5993-3456

Abstract

Emotions are fundamental to human interactions, intricately influencing communication, behavior, and perception. Emotion-Cause Pair Extraction (ECPE) is a critical task in natural language processing that identifies clause pairs associating emotions with their corresponding triggers within textual documents. Unlike traditional Emotion Cause Extraction (ECE), which relies on pre-annotated emotion clauses, our study introduces a novel end-to-end model for ECPE. This innovative approach utilizes the extensive NTCIR-13 English Corpus to establish a robust baseline for ECPE in English, showcasing significant performance improvements over conventional multi-stage methods. Central to our model is the incorporation of Bidirectional Long Short-Term Memory (BiLSTM) networks, enhancing the ability to capture both local and global dependencies in textual sequences. By effectively combining contextual and positional embeddings, our model accurately predicts emotion-cause relationships, paving the way for a deeper understanding of emotional dynamics in conversational contexts and facilitating causal inference. Furthermore, our research highlights superior performance metrics, aligning its efficacy with state-of-the-art techniques in the field. This study advances emotion recognition in natural language processing, providing valuable insights for nuanced analyses of human emotions within textual data. Additionally, our findings enhance understanding of emotional intelligence in user interaction modeling and conversational AI applications. Through the public availability of our dataset and model, we aim to foster collaboration and further research in this vital area, ultimately improving the capacity for emotional understanding in applications ranging from sentiment analysis to interactive learning.


Keywords:

Emotional intelligence, Emotion-Cause Pair Extraction (ECPE), Bidirectional Long Short-Term Memory, Emotional dynamics, Natural language processing, Conversational Analysis

[1] Alswaidan N., Menai M. E. B.: A survey of state-of-the-art approaches for emotion recognition in text. Knowledge and Information Systems 62(8), 2020, 2937–2987 [https://doi.org/10.1007/s10115-020-01449-0].
  Google Scholar

[2] Bahdanau D., Cho K., Bengio Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473. 2014.
  Google Scholar

[3] Chen F., Shi Z., Yang Z., Huang Y.: Recurrent synchronization network for emotion-cause pair extraction. Knowledge-Based Systems 238, 2022, 107965.
  Google Scholar

[4] Chen X., Li Q., Wang J.: A unified sequence labeling model for emotion cause pair extraction. 28th International Conference on Computational Linguistics, 2020, 208–218.
  Google Scholar

[5] Cheng Z., Jiang Z., Yin Y., Yu H., Gu Q.: A symmetric local search network for emotion-cause pair extraction. 28th International Conference on Computational Linguistics, 2020, 139–149.
  Google Scholar

[6] Colombo P., Witon W., Modi A., Kennedy J., Kapadia M.: Affect-driven dialog generation. Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Minnesota, Minneapolis 2019, 3734–3743 [https://doi.org/10.18653/v1/N19-1374].
  Google Scholar

[7] Devlin J.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
  Google Scholar

[8] Ding Z., Xia R., Yu J.: ECPE-2D: Emotion-cause pair extraction based on joint two-dimensional representation, interaction and prediction. 58th Annual Meeting of the Association for Computational Linguistics, 2020, 3161–3170 [https://doi.org/10.18653/v1/2020.acl-main.288].
  Google Scholar

[9] Ding Z., Xia R., Yu J.: End-to-end emotion-cause pair extraction based on sliding window multi-label learning. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020, 3574–3583.
  Google Scholar

[10] Fan C., Yuan C., Du J., Gui L., Yang M., Xu R.: Transition-based directed graph construction for emotion-cause pair extraction. 58th Annual Meeting of the Association for Computational Linguistics, 2020, 3707–3717.
  Google Scholar

[11] Gao K., Xu H., Wang J.: A rule-based approach to emotion cause detection for chinese micro-blogs. Expert Syst. Appl. 42(9), 2015, 4517–4528.
  Google Scholar

[12] Gao Q., Hu J., Xu R., Gui L., He Y., Wong K.-F., Lu Q.: Overview of NTCIR-13 ECA Task. 13th NTCIR Conference on Evaluation of Information Access Technologies. Japan, Tokyo 2017, 361–366.
  Google Scholar

[13] Ghosh S., Chollet M., Laksana E., Morency L. P., Scherer S.: Affect-LM: A neural language model for customizable affective text generation. 55th Annual Meeting of the Association for Computational Linguistics. Canada, Vancouver 2017, 634–642 [https://doi.org/10.18653/v1/P17-1059].
  Google Scholar

[14] Gui Lin, Xu R., Wu D., Lu Q., Zhou Y.: Event-driven emotion cause extraction with corpus construction. Wong K.-F. et al. (eds): Social Media Content Analysis: Natural Language Processing and Beyond. World Scientific, 2018, 145–160.
  Google Scholar

[15] Kingma D. P., Ba J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
  Google Scholar

[16] Li W. et al.: ECPEC: Emotion-Cause Pair Extraction in Conversations. IEEE Transactions on Affective Computing 14(03), 2023, 1754–1765.
  Google Scholar

[17] Li X., Song K., Feng S., Wang D., Zhang Y.: A co-attention neural network model for emotion cause analysis with emotional context awareness. Conference on Empirical Methods in Natural Language Processing, 2018, 4752–4757.
  Google Scholar

[18] Modi A., Kapadia M., Fidaleo D. A., Kennedy J. R., Witon W., Colombo P.: Affect-driven dialog generation. U.S. Patent 10,818,312 B2, October 27, 2020.
  Google Scholar

[19] Neumann M. P. M., Iyyer M., Gardner M., Clark C., Lee K., Zettlemoyer L.: Deep contextualized word representations. arXiv preprint arXiv:1802.05365, 2018.
  Google Scholar

[20] Pennington J., Socher R., Manning C. D.: Glove: Global vectors for word representation. Conference on Empirical Methods in Natural Language processing – EMNLP, 2014, 1532–1543.
  Google Scholar

[21] Singh I., Barkati A., Goswamy T., Modi A.: Adapting a language model for controlled affective text generation. arXiv preprint arXiv:2011.04000 (2020).
  Google Scholar

[22] Strapparava C., Mihalcea R.: Learning to identify emotions in text. ACM symposium on Applied computing, 2008, 1556–1560.
  Google Scholar

[23] Vaswani A. et al.: Attention is all you need. Advances in neural information processing systems. 30th Advances in Neural Information Processing Systems – NIPS, 2017, 5998–6008.
  Google Scholar

[24] Witon W., Colombo P., Modi A., Kapadia M.: Disney at IEST 2018: Predicting emotions using an ensemble. 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Belgium, Brussels 2018, 248–253 [https://doi.org/10.18653/v1/P17].
  Google Scholar

[25] Xia R., Ding Z.: Emotion-cause pair extraction: A new task to emotion analysis in texts. arXiv preprint arXiv:1906.01267 (2019).
  Google Scholar

[26] Zhang Z.: Improved adam optimizer for deep neural networks. IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), 2018.
  Google Scholar

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Published
2024-12-21

Cited by

Chandra, R. M., Neelaiahgari, G. V. S. T., & Vanapalli, S. S. (2024). EXTRACTING EMOTION-CAUSE PAIRS: A BILSTM-DRIVEN METHODOLOGY. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 14(4), 97–103. https://doi.org/10.35784/iapgos.6679

Authors

Raga Madhuri Chandra 
chragamadhuri@vrsiddhartha.ac.in
Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India
https://orcid.org/0000-0002-3687-0783

Authors

Giri Venkata Sai Tej Neelaiahgari 

Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India
https://orcid.org/0009-0009-5785-0413

Authors

Satya Sumanth Vanapalli 

Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India
https://orcid.org/0009-0006-5993-3456

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