Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/136899
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Type: Journal article
Title: A survey of sentiment analysis in social media
Author: Yue, L.
Chen, W.
Li, X.
Zuo, W.
Yin, M.
Citation: Knowledge and Information Systems, 2019; 60(2):617-663
Publisher: Springer London
Issue Date: 2019
ISSN: 0219-3116
0219-3116
Statement of
Responsibility: 
Lin Yue, Weitong Chen, Xue Li, Wanli Zuo, Minghao Yin
Abstract: Sentiments or opinions from social media provide the most up-to-date and inclusive information, due to the proliferation of social media and the low barrier for posting the message. Despite the growing importance of sentiment analysis, this area lacks a concise and systematic arrangement of prior efforts. It is essential to: (1) analyze its progress over the years, (2) provide an overview of the main advances achieved so far, and (3) outline remaining limitations. Several essential aspects, therefore, are addressed within the scope of this survey. On the one hand, this paper focuses on presenting typical methods from three different perspectives (task-oriented, granularity-oriented, methodology-oriented) in the area of sentiment analysis. Specifically, a large quantity of techniques and methods are categorized and compared. On the other hand, different types of data and advanced tools for research are introduced, as well as their limitations. On the basis of these materials, the essential prospects lying ahead for sentiment analysis are identified and discussed.
Keywords: Sentiment analysis; Social media; Data mining; Machine learning; Survey
Rights: © Springer-Verlag London Ltd., part of Springer Nature 2018
DOI: 10.1007/s10115-018-1236-4
Grant ID: http://purl.org/au-research/grants/arc/DP160104075
Published version: http://dx.doi.org/10.1007/s10115-018-1236-4
Appears in Collections:Computer Science publications

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