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CommuniMents: A Framework for Detecting Community Based Sentiments for Events

Author

Listed:
  • Muhammad Aslam Jarwar

    (Department of Computer Sciences, Quaid-i-Azam University, Islamabad, Pakistan & Department of Information and Communications Engineering, Hankuk University of Foreign Studies (HUFS), Seoul, South Korea)

  • Rabeeh Ayaz Abbasi

    (Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia & Department of Computer Sciences, Quaid-i-Azam University, Islamabad, Pakistan)

  • Mubashar Mushtaq

    (Department of Computer Science, Forman Christian College (A Chartered University), Lahore, Pakistan & Department of Computer Sciences, Quaid-i-Azam University, Islamabad, Pakistan)

  • Onaiza Maqbool

    (Department of Computer Sciences, Quaid-i-Azam University, Islamabad, Pakistan)

  • Naif R. Aljohani

    (Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia)

  • Ali Daud

    (Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia & Department of Computer Science and Software Engineering, International Islamic University, Islamabad, Pakistan)

  • Jalal S. Alowibdi

    (Faculty of Computing and Information Technology, University of Jeddah, Jeddah, Saudi Arabia)

  • J.R. Cano

    (Department of Computer Science, University of Jaén, Jaén, Spain)

  • S. García

    (Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain)

  • Ilyoung Chong

    (Department of Information and Communications Engineering, Hankuk University of Foreign Studies (HUFS), Seoul, South Korea)

Abstract

Social media has revolutionized human communication and styles of interaction. Due to its effectiveness and ease, people have started using it increasingly to share and exchange information, carry out discussions on various events, and express their opinions. Various communities may have diverse sentiments about events and it is an interesting research problem to understand the sentiments of a particular community for a specific event. In this article, the authors propose a framework CommuniMents which enables us to identify the members of a community and measure the sentiments of the community for a particular event. CommuniMents uses automated snowball sampling to identify the members of a community, then fetches their published contents (specifically tweets), pre-processes the contents and measures the sentiments of the community. The authors perform qualitative and quantitative evaluation for a variety of real world events to validate the effectiveness of the proposed framework.

Suggested Citation

  • Muhammad Aslam Jarwar & Rabeeh Ayaz Abbasi & Mubashar Mushtaq & Onaiza Maqbool & Naif R. Aljohani & Ali Daud & Jalal S. Alowibdi & J.R. Cano & S. García & Ilyoung Chong, 2017. "CommuniMents: A Framework for Detecting Community Based Sentiments for Events," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 13(2), pages 87-108, April.
    • Handle: RePEc:igg:jswis0:v:13:y:2017:i:2:p:87-108
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    Citations

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    Cited by:

    1. Saeed-Ul Hassan & Timothy D. Bowman & Mudassir Shabbir & Aqsa Akhtar & Mubashir Imran & Naif Radi Aljohani, 2019. "Influential tweeters in relation to highly cited articles in altmetric big data," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 481-493, April.
    2. Masood, Muhammad Ali & Abbasi, Rabeeh Ayaz, 2021. "Using graph embedding and machine learning to identify rebels on twitter," Journal of Informetrics, Elsevier, vol. 15(1).
    3. Sunil Kumar & Ilyoung Chong, 2018. "Correlation Analysis to Identify the Effective Data in Machine Learning: Prediction of Depressive Disorder and Emotion States," IJERPH, MDPI, vol. 15(12), pages 1-24, December.

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