Deep learning for sentiment analysis

Research and industry are becoming more and more interested in finding automatically the polarised opinion of the general public regarding a specific subject. The advent of social networks has opened the possibility of having access to massive blogs, recommendations, and reviews. The challenge is to...

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Bibliographic Details
Published in:Language and linguistics compass Vol. 10; no. 12; pp. 701 - 719
Main Author: Rojas-Barahona, Lina Maria
Format: Journal Article
Language:English
Published: Oxford Blackwell Publishing Ltd 01.12.2016
Wiley Subscription Services, Inc
Subjects:
Blogs
Business
Computer generated language analysis
Data mining
Deep learning
General public
Learning
Natural language processing
Polarity
Sentiment analysis
Social networks
Subjectivity
ISSN:1749-818X, 1749-818X
Online Access:Get full text
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Summary:Research and industry are becoming more and more interested in finding automatically the polarised opinion of the general public regarding a specific subject. The advent of social networks has opened the possibility of having access to massive blogs, recommendations, and reviews. The challenge is to extract the polarity from these data, which is a task of opinion mining or sentiment analysis. The specific difficulties inherent in this task include issues related to subjective interpretation and linguistic phenomena that affect the polarity of words. Recently, deep learning has become a popular method of addressing this task. However, different approaches have been proposed in the literature. This article provides an overview of deep learning for sentiment analysis in order to place these approaches in context.
Bibliography:Industry
ark:/67375/WNG-VCVKFDXB-W
ArticleID:LNC312228
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Competitivity General Direction
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ObjectType-Article-1
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ISSN:1749-818X
1749-818X
DOI:10.1111/lnc3.12228