A survey of dialogic emotion analysis: Developments, approaches and perspectives
Chenquan Gan, Jiahao Zheng, Qingyi Zhu, Yang Cao, Ye Zhu
Abstract
Dialogic emotion analysis is an emerging and important research field in natural language processing. It aims to understand and process emotions in various forms of dialogue, such as human-human conversations, human–machine interactions, and chatbot responses. However, dialogic emotion analysis faces many challenges, such as the diversity of dialogue genres, the complexity of emotional expressions, and the difficulty of capturing the emotional needs of dialogue participants. Moreover, the current dialogue systems lack the ability to analyze emotions effectively and appropriately in different dialogue contexts. Therefore, a comprehensive review of the existing research on dialogic emotion analysis is needed. This survey aims to review dialogic emotion analysis methods based on natural language processing from 2017 to 2024. The review process follows the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA). We summarize the research methods and emphasize their main research contributions. In addition, we also discuss current research trends and possible future research directions, as well as the impact of personal traits on emotions and potential ethical issues.