Abstract
Emotion Cause Extraction (ECE) is an emerging hot topic in the field of sentiment analysis. The purpose of the ECE task is to extract the causes of emotions in the text according to the text and given emotions. Emotional Cause Pair Extraction (ECPE) is a brand-new research problem on ECE, whose main purpose is to extract the emotion clauses and emotion cause clauses in texts at the same time. At present, the ECPE task has received extensive attentions from both academia and industry communities. The existing ECPE researches are mainly carried out on online news report corpus. However, this kind of corpus has several limitations, such as the small scale and rough tagging granularity. In this paper, we construct an emotion cause extraction dataset containing 5,195 COVID-19 pandemic-related discussion posts collected from Sina Weibo, and leverage 10 state-of-the-art models to perform ECE and ECPE tasks on this new dataset. We find that the performances of most existing models on our newly constructed dataset decrease dramatically compared with the reported results in the online news dataset. We further analyze the causes of this phenomenon.
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References
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. 17(1), 6:1–6:19 (2017)
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: AAAI, pp. 6343–6350 (2019)
Ding, Z., Xia, R., Yu, J.: ECPE-2D: emotion-cause pair extraction based on joint two-dimensional representation, interaction and prediction. In: Jurafsky, D., Chai, J., Schluter, N., Tetreault, J.R. (eds.) ACL, pp. 3161–3170 (2020)
Ding, Z., Xia, R., Yu, J.: End-to-end emotion-cause pair extraction based on sliding window multi-label learning. In: Webber, B., Cohn, T., He, Y., Liu, Y. (eds.) EMNLP, pp. 3574–3583 (2020)
Fan, C., Yuan, C., Du, J., Gui, L., Yang, M., Xu, R.: Transition-based directed graph construction for emotion-cause pair extraction. In: Jurafsky, D., Chai, J., Schluter, N., Tetreault, J.R. (eds.) ACL, pp. 3707–3717 (2020)
Gao, Q.: Overview of NTCIR-13 ECA task (2017)
Gui, L., Hu, J., He, Y., Xu, R., Lu, Q., Du, J.: A question answering approach to emotion cause extraction. CoRR arXiv:1708.05482 (2017)
Gui, L., Wu, D., Xu, R., Lu, Q., Zhou, Y.: Event-driven emotion cause extraction with corpus construction. In: EMNLP, pp. 1639–1649 (2016)
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)
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, CAAGET 2010, pp. 45–53. Association for Computational Linguistics, New York (2010)
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)
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: Riloff, E., Chiang, D., Hockenmaier, J., Tsujii, J. (eds.) EMNLP, pp. 4752–4757 (2018)
Liang, Y., Xu, L., Huang, T.: Sentiment tendency analysis of NPC&CPPCC in German news. In: Ni, W., Wang, X., Song, W., Li, Y. (eds.) WISA 2019. LNCS, vol. 11817, pp. 298–308. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30952-7_30
Liu, W., Zhang, M.: Semi-supervised sentiment classification method based on Weibo social relationship. In: Ni, W., Wang, X., Song, W., Li, Y. (eds.) WISA 2019. LNCS, vol. 11817, pp. 480–491. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30952-7_47
Wei, P., Zhao, J., Mao, W.: Effective inter-clause modeling for end-to-end emotion-cause pair extraction. In: Jurafsky, D., Chai, J., Schluter, N., Tetreault, J.R. (eds.) ACL, pp. 3171–3181 (2020)
Xia, R., Ding, Z.: Emotion-cause pair extraction: a new task to emotion analysis in texts. In: Korhonen, A., Traum, D.R., Màrquez, L. (eds.) ACL, pp. 1003–1012. Association for Computational Linguistics (2019)
Xia, R., Zhang, M., Ding, Z.: RTHN: a RNN-transformer hierarchical network for emotion cause extraction. In: Kraus, S. (ed.) IJCAI, pp. 5285–5291 (2019)
Acknowledgments
The work was supported by the National Natural Science Foundation of China (61872074, 61772122), the Fundamental Research Funds for the Central Universities (N180716010, N2124001), and the National Training Program of Innovation and Entrepreneurship for Undergraduates (S202110145126).
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Liu, Z., Jin, Z., Wei, C., Li, X., Feng, S. (2021). CoEmoCause: A Chinese Fine-Grained Emotional Cause Extraction Dataset. In: Xing, C., Fu, X., Zhang, Y., Zhang, G., Borjigin, C. (eds) Web Information Systems and Applications. WISA 2021. Lecture Notes in Computer Science(), vol 12999. Springer, Cham. https://doi.org/10.1007/978-3-030-87571-8_45
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DOI: https://doi.org/10.1007/978-3-030-87571-8_45
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