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 journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 987-996 |
Journal / Publication | Information and Management |
Volume | 53 |
Issue number | 8 |
Online published | 8 Jun 2016 |
Publication status | Published - Dec 2016 |
Link(s)
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
Citation Format(s)
User opinion classification in social media : A global consistency maximization approach. / Li, Jiexun; Li, Xin; Zhu, Bin.
In: Information and Management, Vol. 53, No. 8, 12.2016, p. 987-996.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review