Community discovery using social links and author-based sentiment topics

Social networking services are attracting increasing interest in the domain of community discovery. In social networks, the interactions among users are very frequent by sending emails, posting tweets, and sharing comments online, etc. Such networks usually include rich sentiment information, which can provide us with useful resources for identifying communities with different sentiment-topic distributions. Most conventional community discovery methods only consider the social links among users, which ignore the valuable content information. Recent studies have focused on community detection by integrating both links and content. However, most of these methods are not available for identifying sentiment-topic based communities. In this paper, we propose two novel community discovery models by combining social links, author based topics and sentiment information to identify communities with different sentiment-topic distributions. We evaluate our models on two real-world datasets, and the experimental results demonstrate the effectiveness of our proposed models.