dc.contributor.authorGoh, Sherwin Si Win
dc.date.accessioned2017-04-18T11:01:51Z
dc.date.available2017-04-18T11:01:51Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/10356/70292
dc.description.abstractSports organisations must understand sports fans’ motivations behind their social media interactions to successfully harness it as a tool for relationship marketing. Few studies have directly analysed sports fans’ contributions on social media, and of these, most examined supporters’ contributions by reading and sorting each comment. Text mining conducts automated analysis of social media comments, making it much less labour and time consuming than traditional content analysis methods. However, its use has hardly been applied to sport. The purpose of this study is to investigate the suitability of text mining in interpreting supporters’ motivations from their social media contributions. Lee Chong Wei’s loss in the 2016 Rio Olympics badminton men’s singles final was the setting for this study. A qualitative approach using text mining and cluster analysis with R Studio was employed to analyse 16972 English tweets under #chongweiourhero, with the results presented as a hierarchical cluster dendrogram. Results indicate that Lee’s fans had the motives of communicating with him, expressing emotion, offering consolation, expressing appreciation and expressing unity when responding to Lee’s loss at the 2016 Olympics on social media. These categories are similar to those found in the study by Kee et al. (2016) which studied the motivations of Lee’s supporters in a similar setting at the 2012 Olympics, but through traditional content analysis methods. The present study validates the suitability of text mining in interpreting supporters’ motives from their social media contributions, and supports it use in aiding sports organizations to harness social media as marketing tool.en_US
dc.format.extent27 p.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Social sciences::Journalism::Reporting on sportsen_US
dc.titleUnderstanding sports fans' motivations by text mining #ChongWeiOurHeroen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorKee Ying Hwa Adrianen_US
dc.contributor.schoolNational Institute of Educationen_US
dc.description.degreeBachelor of Science (Sport Science and Management)en_US


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