A Survey on Opinion Mining: From Stance to Product Aspect

被引:43
作者
Wang, Rui [1 ]
Zhou, Deyu [1 ]
Jiang, Mingmin [1 ]
Si, Jiasheng [1 ]
Yang, Yang [1 ]
机构
[1] Southeast Univ, Sch Comp Sci & Engn, Key Lab Comp Network & Informat Integrat, Minist Educ, Nanjing 210096, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
Opinion mining; stance detection; product aspect mining; topic model; deep neural network; SENTIMENT ANALYSIS; COEXTRACTION; EXTRACTION; TWEETS;
D O I
10.1109/ACCESS.2019.2906754
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the prevalence of social media and online forum, opinion mining, aiming at analyzing and discovering the latent opinion in user-generated reviews on the Internet, has become a hot research topic. This survey focuses on two important subtasks in this field, stance detection and product aspect mining, both of which can be formalized as the problem of the triple 'target, aspect, opinion' extraction. In this paper, we first introduce the general framework of opinion mining and describe the evaluation metrics. Then, the methodologies for stance detection on different sources, such as online forum and social media are discussed. After that, approaches for product aspect mining are categorized into three main groups which are corpus level aspect extraction, corpus level aspect, and opinion mining, and document level aspect and opinion mining based on the processing units and tasks. And then we discuss the challenges and possible solutions. Finally, we summarize the evolving trend of the reviewed methodologies and conclude the survey.
引用
收藏
页码:41101 / 41124
页数:24
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