摘要
Textual Emotion Analysis(TEA)aims to extract and analyze user emotional states in texts.Various Deep Learning(DL)methods have developed rapidly,and they have proven to be successful in many fields such as audio,image,and natural language processing.This trend has drawn increasing researchers away from traditional machine learning to DL for their scientific research.In this paper,we provide an overview of TEA based on DL methods.After introducing a background for emotion analysis that includes defining emotion,emotion classification methods,and application domains of emotion analysis,we summarize DL technology,and the word/sentence representation learning method.We then categorize existing TEA methods based on text structures and linguistic types:text-oriented monolingual methods,text conversations-oriented monolingual methods,text-oriented cross-linguistic methods,and emoji-oriented cross-linguistic methods.We close by discussing emotion analysis challenges and future research trends.We hope that our survey will assist readers in understanding the relationship between TEA and DL methods while also improving TEA development.
基金
This work is partially supported by the National Natural Science Foundation of China under Grant Nos.61876205 and 61877013
the Ministry of Education of Humanities and Social Science project under Grant Nos.19YJAZH128 and 20YJAZH118
the Science and Technology Plan Project of Guangzhou under Grant No.201804010433
the Bidding Project of Laboratory of Language Engineering and Computing under Grant No.LEC2017ZBKT001.