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, JR Cano, S Garcia, Ilyoung Chong

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)87-108
Number of pages22
JournalInternational Journal on Semantic Web and Information Systems
Issue number2
Publication statusPublished - 1 Apr 2017

Research Beacons, Institutes and Platforms

  • Cathie Marsh Institute


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