A Sentiment-Based Approach to Twitter User Recommendation

Nowadays, the emerging popularity of Social Web raises new application areas for recommender systems. The aim of a social user recommendation is to suggest new friends having similar interests. In order to identify such interests, current recommender algorithms exploit social network information or the similarity of user-generated content. The rationale of this work is that users may share similar interests but have different opinions on them. As a result, considering the contribution of user sentiments, can yield benefits in recommending possible friends to follow. In this paper we propose a user recommendation technique based on a novel weighting function, we named sentimentvolume-objectivity (SVO) function, which takes into account not only user interests, but also his sentiments. Such function allows us to build richer user profiles to employ in the recommendation process than other content-based approaches. Preliminary results based on a comparative analysis show the benefits of the advanced approach in comparison with some state-of-the-art user recommender systems.