A survey on textual emotion cause extraction in social networks - Sancheng Peng; Lihong Cao; Guojun Wang; Zhouhao Ouyang; Yongmei Zhou; Shui Yu (Digital Communications and Networks)
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.