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GLIO: A New Method for Grouping Like-Minded Users

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Transactions on Computational Collective Intelligence XVIII

Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 9240))

Abstract

Grouping like-minded users is one of the emerging problems in Social Network Analysis. Indeed, it gives a good idea about group formation and social network evolution. Also, it explains various social phenomena and leads to many applications, such as friends suggestion and collaborative filtering. In this paper, we introduce a novel unsupervised method for grouping like-minded users within social networks. Such a method detects groups of users sharing the same interest centers and having similar opinions. In fact, the proposed method is based on extracting the interest centers and retrieving the polarities from the user’s textual posts. We validate our results by employing multiple clustering evaluation measures (recall, precision, F-score and Rand-Index). We compare our algorithm to a number of other clustering algorithms and opinion detection API. Results prove that the algorithm presented is efficient.

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Notes

  1. 1.

    http://www.statisticbrain.com/twitter-statistics.

  2. 2.

    http://help.sentiment140.com/api.

  3. 3.

    http://www.sananalytics.com/lab/twitter-sentiment/.

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Authors and Affiliations

  1. MIRACL Laboratory Higher Institute of Computer Science and Multimedia, University of Sfax, BP 1030, Sfax, Tunisia

    Soufiene Jaffali, Hanen Ameur, Salma Jamoussi & Abdelmajid Ben Hamadou

Authors
  1. Soufiene Jaffali
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  2. Hanen Ameur
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  3. Salma Jamoussi
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  4. Abdelmajid Ben Hamadou
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Corresponding author

Correspondence to Soufiene Jaffali .

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Editors and Affiliations

  1. Wroclaw University of Technology, Department of Information Systems, Wroclaw, Poland

    Ngoc Thanh Nguyen

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Jaffali, S., Ameur, H., Jamoussi, S., Hamadou, A.B. (2015). GLIO: A New Method for Grouping Like-Minded Users. In: Nguyen, N. (eds) Transactions on Computational Collective Intelligence XVIII. Lecture Notes in Computer Science(), vol 9240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48145-5_3

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  • DOI: https://doi.org/10.1007/978-3-662-48145-5_3

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