A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pages 5614–5624, Hong Kong, China, November 3–7, 2019. c⃝2019 Association for Computational Linguistics 5614 A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis Chuang Fan1,5, Hongyu Yan1,5, Jiachen Du1,5, Lin Gui2, Lidong Bing3 Min Yang4, Ruifeng Xu1,5,6∗, Ruibin Mao7 1Harbin Institute of Technology (Shenzhen), China 2University of Warwick, UK 3R&D Center Singapore, Machine Intelligence Technology, Alibaba DAMO Academy 4Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences 5Joint Lab of Harbin Institute of Technology and RICOH 6Peng Cheng Laboratory, China 7Shenzhen Securities Information Co.,Ltd, China Abstract Emotion cause analysis, which aims to iden- tify the reasons behind emotions, is a key topic in sentiment analysis. A variety of neural network models have been proposed recently, however, these previous models mostly focus on the learning architecture with local tex- tual information, ignoring the discourse and prior knowledge, which play crucial roles in human text comprehension. In this paper, we propose a new method to extract emo- tion cause with a hierarchical neural model and knowledge-based regularizations, which aims to incorporate discourse context infor- mation and restrain the parameters by senti- ment lexicon and common knowledge. The experimental results demonstrate that our pro- posed method achieves the state-of-the-art per- formance on two public datasets in different languages (Chinese and English), outperform- ing a number of competitive baselines by at least 2.08% in F-measure. 1 Introduction Sentiment analysis has gained increasing popular- ity in recent years due to many useful applications (Pang and Lee, 2007). The goal of sentiment anal- ysis is to classify the sentiment polarity of a given text as positive, negative, neutral, or more fine- grained classes (Kim, 2014; Li et al., 2015; Yang et al., 2016; Zhou et al., 2016; Chen et al., 2017; Qian et al., 2017; Li et al., 2018b; Yu et al., 2018). Most of these researches have assumed that emo- tion expressions are already observed and try to identify the emotion categories from text. How- ever, in practice, such as in product comments or political reviews, we may care more about the rea- son why the customers or critics hold the emotion rather than a simple category label. Because they can improve the quality of the products or services ∗ Corresponding Author: xuruifeng@hit.edu.cn according to the emotion cause provided by users. Emotion cause analysis (ECA) aims to identify the reasons behind a certain emotion expression in an event text, for example: Ex.1 When the children saw the gifts I prepared carefully, (c−2)|they cheered happily and hugged me. (c−1)| I was full of happiness. (c0) Here, Ex.1 shows a document with three clauses marked as (c−2), (c−1), and (c0). The goal of ECA is to determine which clause contains emo- tion cause (e.g., (c−1)) for an emotion word (e.g., happiness in (c0)). Previous approaches for emotion cause analysis mostly depend on rule-based methods (Lee et al., 2010; Chen et al., 2010) and machine learning …

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