Recent advances in deep learning based sentiment analysis

查看全文 作　　者：YUAN [1]JianHua;WU [1]Yang;LU [1]Xin;ZHAO [1]YanYan;QIN [1,2]Bing;LIU [1]Ting

机构地区：[1]Research Center for Social Computing and Information Retrieval,Harbin Institute of Technology,Harbin 150001,China;[2]Pengcheng Lab,Shenzhen 518066,China

出　　处：《Science China(Technological Sciences)》索引2020年第63卷第10期,共24页

基　　金：the National Key R&D Program of China(Grant No.2018YFB1005103);the National Natural Science Foundation of China(Grant Nos.61632011 and 61772153)supported by China Scholarship Council(CSC)during a visit to the University of Copenhagen。 摘　　要：Sentiment analysis is one of the most popular research areas in natural language processing.It is extremely useful in many applications,such as social media monitoring and e-commerce.Recent application of deep learning based methods has dramatically changed the research strategies and improved the performance of many traditional sentiment analysis tasks,such as sentiment classification and aspect based sentiment analysis.Moreover,it also pushed the boundary of various sentiment analysis task,including sentiment classification of different text granularities and in different application scenarios,implicit sentiment analysis,multimodal sentiment analysis and generation of sentiment-bearing text.In this paper,we give a brief introduction to the recent advance of the deep learning-based methods in these sentiment analysis tasks,including summarizing the approaches and analyzing the dataset.This survey can be well suited for the researchers studying in this field as well as the researchers entering the field. 关键词：COARSE-GRAINED FINE-GRAINED IMPLICIT MULTI-MODAL GENERATION

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Recent advances in deep learning based sentiment analysis

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