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Soft Computing

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19-08-2020 | Methodologies and Application

Emotion cause detection with enhanced-representation attention convolutional-context network

Authors: Yufeng Diao, Hongfei Lin, Liang Yang, Xiaochao Fan, Yonghe Chu, Di Wu, Kan Xu

Published in: Soft Computing | Issue 2/2021

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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.

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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):1–1CrossRef

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):257–269MathSciNetMATH

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, OxfordCrossRef

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):1735–1780CrossRef

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):1742–1749CrossRef

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 2018:124–133CrossRef

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 2018:49–57CrossRef

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 312:218–228CrossRef

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 275:2459–2466CrossRef

Yang M, Qu Q, Chen X et al (2018) Feature-enhanced attention network for target-dependent sentiment classification. Neurocomputing 2018:91–97CrossRef

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 155:1–10CrossRef

Zhou D, Zhang X, Zhou Y, Zhao Q, Geng X (2016) Emotion distribution learning from texts. In: EMNLP, pp 638–647

Title: Emotion cause detection with enhanced-representation attention convolutional-context network
Authors: Yufeng Diao
Hongfei Lin
Liang Yang
Xiaochao Fan
Yonghe Chu
Di Wu
Kan Xu
Publication date: 19-08-2020
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 2/2021
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-020-05223-w

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