CommuniMents: A Framework for Detecting Community Based Sentiments for Events

Muhammad Aslam Jarwar, Rabeeh Ayaz Abbasi, Mubashar Mushtaq, Onaiza Maqbool, Naif R. Aljohani, Ali Daud, Jalal S. Alowibdi, J.R. Cano, S. García and Ilyoung Chong
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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

International Journal on Semantic Web and Information Systems (IJSWIS), 2017, vol. 13, issue 2, 87-108

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.

Date: 2017
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