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Grouping Like-Minded Users Based on Text and Sentiment Analysis

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Computational Collective Intelligence. Technologies and Applications (ICCCI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8733))

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Abstract

With the growth of social media usage, the study of online communities and groups has become an appealing research domain. In this context, grouping like-minded users is one of the emerging problems. Indeed, it gives a good idea about group formation and evolution, explains various social phenomena and leads to many applications, such as link prediction and product suggestion. In this dissertation, we propose 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.

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Author information

Authors and Affiliations

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

    Soufiene Jaffali, Salma Jamoussi & Abdelmajid Ben Hamadou

Authors
  1. Soufiene Jaffali
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  2. Salma Jamoussi
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  3. Abdelmajid Ben Hamadou
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Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Yeungnam University, Korea

    Dosam Hwang  & Jason J. Jung  & 

  2. Institute of Informatics, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Ngoc-Thanh Nguyen

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Jaffali, S., Jamoussi, S., Hamadou, A.B. (2014). Grouping Like-Minded Users Based on Text and Sentiment Analysis. In: Hwang, D., Jung, J.J., Nguyen, NT. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2014. Lecture Notes in Computer Science(), vol 8733. Springer, Cham. https://doi.org/10.1007/978-3-319-11289-3_9

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  • DOI: https://doi.org/10.1007/978-3-319-11289-3_9

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11288-6

  • Online ISBN: 978-3-319-11289-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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