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Deep learning for sentiment analysis
Author(s) -
RojasBarahona Lina Maria
Publication year - 2016
Publication title -
language and linguistics compass
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.619
H-Index - 44
ISSN - 1749-818X
DOI - 10.1111/lnc3.12228
Subject(s) - sentiment analysis , task (project management) , polarity (international relations) , computer science , context (archaeology) , interpretation (philosophy) , natural language processing , artificial intelligence , deep learning , public opinion , order (exchange) , subject (documents) , affect (linguistics) , data science , linguistics , world wide web , history , political science , law , philosophy , genetics , management , archaeology , finance , biology , politics , economics , cell , programming language
Abstract 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.