User opinion classification in social media : A global consistency maximization approach

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

7 Scopus Citations
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Original languageEnglish
Pages (from-to)987-996
Journal / PublicationInformation and Management
Volume53
Issue number8
Online published8 Jun 2016
Publication statusPublished - Dec 2016

Abstract

Social media is a major platform for opinion sharing. In order to better understand and exploit opinions on social media, we aim to classify users with opposite opinions on a topic for decision support. Rather than mining text content, we introduce a link-based classification model, named global consistency maximization (GCM) that partitions a social network into two classes of users with opposite opinions. Experiments on a Twitter data set show that: (1) our global approach achieves higher accuracy than two baseline approaches and (2) link-based classifiers are more robust to small training samples if selected properly.

Research Area(s)

  • Big data, Collective classification, Opinion mining, Social media