iSCUR: Interest and Sentiment-Based Community Detection for User Recommendation on Twitter

The increasing popularity of social networks has encouraged a large number of significant research works on community detection and user recommendation. The idea behind this work is that taking into account users’ attitudes toward their own interests can bring benefits in performing such tasks. In this paper we describe (i) a novel method to infer sentiment-based communities without the requirement of obtaining the whole social structure, and (ii) a community-based approach to user recommendation. We take advantage of the SVO (sentiment-volume-objectivity) user profiling and the weighted Tanimoto similarity to evaluate user similarity for each topic. Afterwards we employ a clustering algorithm based on modularity optimization to find densely connected users and the Adamic-Adar tie strength to finally suggest the most relevant users. Preliminary experimental results on Twitter show the benefits of the proposed approach compared to some state-of-the-art user recommendation techniques.