This article reports our experience in developing a recommender system (RS) able to suggest relevant people to the target user. Such a RS relies on a user profile represented as a set of weighted concepts related to the user's interests. The weighting function, we named sentiment-volume-objectivity (SVO) function, takes into account not only the user's sentiment toward his/her interests, but also the volume and objectivity of related contents. A clustering technique based on modularity optimization enables us to identify the latent sentiment communities. A preliminary experimental evaluation on real-world datasets from Twitter shows the benefits of the proposed approach and allows us to make some considerations about the detected communities.

Gurini, D.F., Gasparetti, F., Micarelli, A., Sansonetti, G. (2015). Analysis of sentiment communities in online networks. In CEUR Workshop Proceedings (pp.17-20). CEUR-WS.

Analysis of sentiment communities in online networks

GASPARETTI, FABIO;MICARELLI, Alessandro;SANSONETTI, GIUSEPPE
2015-01-01

Abstract

This article reports our experience in developing a recommender system (RS) able to suggest relevant people to the target user. Such a RS relies on a user profile represented as a set of weighted concepts related to the user's interests. The weighting function, we named sentiment-volume-objectivity (SVO) function, takes into account not only the user's sentiment toward his/her interests, but also the volume and objectivity of related contents. A clustering technique based on modularity optimization enables us to identify the latent sentiment communities. A preliminary experimental evaluation on real-world datasets from Twitter shows the benefits of the proposed approach and allows us to make some considerations about the detected communities.
Gurini, D.F., Gasparetti, F., Micarelli, A., Sansonetti, G. (2015). Analysis of sentiment communities in online networks. In CEUR Workshop Proceedings (pp.17-20). CEUR-WS.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/300287
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