Abstract
Emotion cause detection is mainly to identify the emotion cause with an emotion expression text, which plays a critical role in building NLP applications. This task is much more difficult than other emotion classification and emotion extraction problems. However, most existing methods only focus on partial information to extract the emotion cause. In this study, we present a new approach to combine the emotion word with its synonyms in order to discover the deep and semantic information by enhanced-representation and attention mechanism. Meanwhile, we propose a new mechanism to introduce the hierarchical context behind emotion word information for extracting emotion cause inspired by a convolution operation. Our proposed framework can extract both enhanced represented emotion level features and context level features to better detect the emotion cause. We have conducted extensive experiments on the emotion cause dataset. Experimental results demonstrate the effectiveness of our proposed model, outperforming a number of competitive baselines by at least 3.39% in F1-measure.
Similar content being viewed by others
Notes
Corpus Url: http://hlt.hitsz.edu.cn/?page_id=694.
http://news.sina.com.cn/society/.
References
Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. Comp Sci
Balahur A, Hermida J M, Tanev H (2013) Detecting implicit emotion expressions from text using ontological resources and lexical learning: 235–255
Carlson A, Betteridge J, Wang RC et al. (2010) Coupled semi-supervised learning for information extraction. In: WDSM, pp 101–110
Chang YC, Chen CC, Hsieh YL, Hsu WL (2015) Linguistic template extraction for recognizing reader-emotion and emotional resonance writing assistance. In: ACL, pp 775–780
Chen Y, Lee SYM, Li S, Huang CR (2010) Emotion cause detection with linguistic constructions. In: COLING, pp 179–187
Chen WF, Chen MH, Chen ML, Lun Wei K (2016) A computer-assistance learning system for emotional wording. IEEE Trans Knowl Data Eng 28(5):1–1
Cheng J, Zhao S, Zhang J et al. (2017) Aspect-level sentiment classification with heat (HiErarchical ATtention) network. ACM, pp 97–106
Das D, Bandyopadhyay S (2011) Emotions on Bengali blog texts: role of holder and topic. In: International conference on advances in social networks analysis and mining, pp 587–592
Dean J, Corrado GS, Monga R et al. (2012) Large scale distributed deep networks. In: International conference on neural information processing systems. Curran Associates Inc, pp 1223–1231
Duchi J, Hazan E, Singer Y (2011) Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. J Mach Learn Res 12(7):257–269
Ekman P (1987) Expression and the nature of emotion. Appr Emot 3:19–344
Gao W, Li S, Lee SYM, Zhou G, Huang CR (2013) Joint learning on sentiment and emotion classification. In: CIKM, pp 1505–1508
Gao K, Xu H, Wang J (2015) A rule-based approach to emotion cause detection for Chinese micro-blogs. Pergamon Press Inc, Oxford
Graves A, Jaitly N, Mohamed A (2013) Hybrid speech recognition with deep bidirectional LSTM. Automatic Speech Recognition and Understanding (ASRU), IEEE Workshop on. In: IEEE, 2013, pp 273–278
Gui L, Wu D, Xu R, Lu Q, Zhou Y (2016) Event-driven emotion cause extraction with corpus construction. In: EMNLP, pp 1639–1649
Gui L, Hu J, He Y et al. (2017) A question answering approach to emotion cause extraction. In: EMNLP
Hermann KM, Kocisky T, Grefenstette E et al. (2015) Teaching machines to read and comprehend. In: Advances in neural information processing systems. pp 1693–1701
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780
Kumar A, Irsoy O, Ondruska P et al (2016) Ask me anything: Dynamic memory networks for natural language processing. In: International conference on machine learning, pp 1378–1387
Lee SYM, Chen Y, Huang CR (2010) 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. Association for computational linguistics: 45–53
Li W, Hua X (2014) Text-based emotion classification using emotion cause extraction. Expert Syst Appl 41(4):1742–1749
Lin-Hong XU, Lin HF, Yang ZH (2007) Text orientation identification based on semantic comprehension. J Chin Inf Process 21(1):96–100
Majumder N, Hazarika D, Gelbukh A et al (2018) Multimodal sentiment analysis using hierarchical fusion with context modeling. Knowl Based Syst 2018:124–133
Mikolov T, Chen K, Corrado G et al. (2013) Efficient estimation of word representations in vector space. In: ICLR, pp 1–12
Rao G, Huang W, Feng Z et al (2018) LSTM with sentence representations for document-level sentiment classification. Neurocomputing 2018:49–57
Ruifeng X, Zou C, Yanzhen Zheng X, Jun LG, Liu B, Wang X (2013) A new emotion dictionary based on the distinguish of emotion expression and emotion cognition. J ChinInf Process 27(6):82–90
Russo I, Caselli T, Rubino F, Boldrini E, Mart´ ınez-Barco P (2011) Emocause: an easy-adaptable approach to emotion cause contexts. In: Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, pp 153–160
Shen T, Zhou T, Long G et al (2018) Disan: directional self-attention network for rnn/cnn-free language understanding. In: AAAI
Song K, Yao T, Ling Q et al (2018) Boosting image sentiment analysis with visual attention. Neurocomputing 312:218–228
Turner JH (2010) On the origins of human emotions: a sociological inquiry into the evolution -of human affect. Stanford University Press Stanford. Stanford University Press Stanford, CA
Tan Z, Wang M, Xie J, et al. (2018) Deep Semantic Role Labeling with Self-Attention. In: AAAI
Xiong S, Lv H, Zhao W et al (2018) Towards Twitter sentiment classification by multi-level sentiment-enriched word embeddings. Neurocomputing 275:2459–2466
Yang M, Qu Q, Chen X et al (2018) Feature-enhanced attention network for target-dependent sentiment classification. Neurocomputing 2018:91–97
Yoon K (2014) Convolutional neural networks for sentence classification. In: EMNLP, pp 1746–1751
Yuan Z, Wu S, Wu F et al (2018) Domain attention model for multi-domain sentiment classification. Knowledge-Based Syst 155:1–10
Zhou D, Zhang X, Zhou Y, Zhao Q, Geng X (2016) Emotion distribution learning from texts. In: EMNLP, pp 638–647
Acknowledgements
This work is partially supported by grant from the Natural Science Foundation of China (No.61632011, 61702080, 61602079, 61772103), the Ministry of Education Humanities and Social Science Project (No.16YJCZH12), the Fundamental Research Funds for the Central Universities (DUT18ZD102), the National Key Research Development Program of China (No. 2016YFB1001103), and China Postdoctoral Science Foundation (No. 2018M631788).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Human and animal rights
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Communicated by V. Loia.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Diao, Y., Lin, H., Yang, L. et al. Emotion cause detection with enhanced-representation attention convolutional-context network. Soft Comput 25, 1297–1307 (2021). https://doi.org/10.1007/s00500-020-05223-w
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-020-05223-w