Context-Aware Social Media User Sentiment Analysis  

作　　者：Bo Liu Shijiao Tang Xiangguo Sun Qiaoyun Chen Jiuxin Cao Junzhou Luo Shanshan Zhao 

机构地区：[1]the School of Computer Science and Engineering,Southeast University,Nanjing 211189,China [2]the Key Laboratory of Computer Network and Information of Ministry of Education of China,Nanjing 211189,China [3]Microsoft Research Asia,Suzhou 215000,China [4]the School of Cyber Science and Engineering,Southeast University,Nanjing 211189,China [5]the Department of Computer Science and Creative Technologies,University of the West of England,Bristol,BS161QY,UK

出　　处：《Tsinghua Science and Technology》2020年第4期528-541,共14页清华大学学报（自然科学版（英文版）

基　　金：supported by the National Key R&D Program of China(No.2017YFB1003000);the National Natural Science Foundation of China(Nos.61972087and 61772133);the National Social Science Foundation of China(No.19@ZH014);Jiangsu Provincial Key Project(No.BE2018706);the Natural Science Foundation of Jiangsu Province(No.SBK2019022870);Jiangsu Provincial Key Laboratory of Network and Information Security(No.BM2003201);Key Laboratory of Computer Network and Information Integration of Ministry of Education of China(No.93K-9).

摘　　要：The user-generated social media messages usually contain considerable multimodal content.Such messages are usually short and lack explicit sentiment words.However,we can understand the sentiment associated with such messages by analyzing the context,which is essential to improve the sentiment analysis performance.Unfortunately,majority of the existing studies consider the impact of contextual information based on a single data model.In this study,we propose a novel model for performing context-aware user sentiment analysis.This model involves the semantic correlation of different modalities and the effects of tweet context information.Based on our experimental results obtained using the Twitter dataset,our approach is observed to outperform the other existing methods in analysing user sentiment.The user-generated social media messages usually contain considerable multimodal content.Such messages are usually short and lack explicit sentiment words.However, we can understand the sentiment associated with such messages by analyzing the context, which is essential to improve the sentiment analysis performance.Unfortunately, majority of the existing studies consider the impact of contextual information based on a single data model.In this study, we propose a novel model for performing context-aware user sentiment analysis.This model involves the semantic correlation of different modalities and the effects of tweet context information.Based on our experimental results obtained using the Twitter dataset, our approach is observed to outperform the other existing methods in analysing user sentiment.

关 键 词：SOCIAL media SENTIMENT analysis MULTIMODAL data CONTEXT-AWARE TOPIC model 

分 类 号：TP393.092[自动化与计算机技术—计算机应用技术] TP391.1[自动化与计算机技术—计算机科学与技术]