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A Survey on Opinion Mining: From Stance to Product Aspect
- Source :
- IEEE Access, Vol 7, Pp 41101-41124 (2019)
- Publication Year :
- 2019
- Publisher :
- IEEE, 2019.
-
Abstract
- 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.
- Subjects :
- General Computer Science
Computer science
02 engineering and technology
Field (computer science)
Opinion mining
Document level
product aspect mining
0202 electrical engineering, electronic engineering, information engineering
General Materials Science
Social media
Product (category theory)
Electrical and Electronic Engineering
topic model
Stance detection
business.industry
05 social sciences
Sentiment analysis
General Engineering
deep neural network
Online forum
Data science
020201 artificial intelligence & image processing
The Internet
lcsh:Electrical engineering. Electronics. Nuclear engineering
0509 other social sciences
050904 information & library sciences
business
stance detection
lcsh:TK1-9971
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....c983b0e4d1a198f37c4135b8984f3604