A survey on textual emotion cause extraction in social networks^☆

Abstract

With the rapid development of web technology, Social Networks (SNs) have become one of the most popular platforms for users to exchange views and to express their emotions. More and more people are used to commenting on a certain hot spot in SNs, resulting in a large amount of texts containing emotions. Textual Emotion Cause Extraction (TECE) aims to automatically extract causes for a certain emotion in texts, which is an important research issue in natural language processing. It is different from the previous tasks of emotion recognition and emotion classification. In addition, it is not limited to the shallow-level emotion classification of text, but to trace the emotion source. In this paper, we provide a survey for TECE. First, we introduce the development process and classification of TECE. Then, we discuss the existing methods and key factors for TECE. Finally, we enumerate the challenges and developing trend for TECE.

Keywords

Text / Emotion / Emotion cause / Machine learning / Deep learning

CRediT authorship contribution statement

Sancheng Peng: Writing - original draft, Methodology, Investigation, Funding acquisition. Lihong Cao: Writing - review & editing. Guojun Wang: Writing - review & editing, Funding acquisition. Zhouhao Ouyang: Project administration, Funding acquisition. Yongmei Zhou: Resources, Investigation. Shui Yu: Writing - review & editing, Methodology.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work is partially supported by the National Natural Science Foundation of China under Grant No. 62372121, the Ministry of education of Humanities and Social Science project under Grant No. 20YJAZH118, the National Key Research and Development Program of China under Grant No. 2020YFB1005804, and the MOE Project at Center for Linguistics and Applied Linguistics, Guangdong University of Foreign Studies.

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