User opinion classification in social media: A global consistency maximization approach
提出一种基于链接的全局一致性最大化模型，通过划分社交网络来分类用户观点，实验表明该方法在Twitter数据集上比基线方法更准确，且对少量训练样本更稳健。
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.