Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4972632 | Information & Management | 2016 | 32 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Information Systems
Authors
Jiexun Li, Xin Li, Bin Zhu,