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 peculiar users’ attitudes (i.e., sentiments, opinions or ways of thinking) 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 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 to follow. Preliminary experimental results on Twitter reveal the benefits of our approach compared to some state-of-the-art user recommendation techniques.

FELTONI GURINI, D., Gasparetti, F., Micarelli, A., Sansonetti, G. (2014). iSCUR: Interest and Sentiment-Based Community Detection for User Recommendation on Twitter. In User Modeling, Adaptation, and Personalization (pp.314-319). SPRINGER-Verlag [10.1007/978-3-319-08786-3_27].

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

FELTONI GURINI, DAVIDE;GASPARETTI, FABIO;MICARELLI, Alessandro;SANSONETTI, GIUSEPPE
2014-01-01

Abstract

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 peculiar users’ attitudes (i.e., sentiments, opinions or ways of thinking) 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 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 to follow. Preliminary experimental results on Twitter reveal the benefits of our approach compared to some state-of-the-art user recommendation techniques.
2014
978-3-319-08786-3
FELTONI GURINI, D., Gasparetti, F., Micarelli, A., Sansonetti, G. (2014). iSCUR: Interest and Sentiment-Based Community Detection for User Recommendation on Twitter. In User Modeling, Adaptation, and Personalization (pp.314-319). SPRINGER-Verlag [10.1007/978-3-319-08786-3_27].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/187771
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