Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/55514
Title: A case study on mining social media data
Authors: Chan, HK
Lacka, E
Yee, RWY 
Lim, MK
Keywords: Cluster analysis
Content analysis
Social media
Text mining
Issue Date: 2014
Publisher: IEEE Computer Society
Source: 2014 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2014, 9-12 December 2014, 7058707, p. 593-596 How to cite?
Abstract: In recent years, usage of social media websites have been soaring. This trend not only limits to personal but corporate web-sites. The latter platforms contain an enormous amount of data posted by customers or users. Without a surprise, the data in corporate social media web-sites are normally link to the products or services provided by the companies. Therefore, the data can be utilized for the sake of companies' benefits. For example, operations management research and practice with the objective to make decisions on product and process design. Nevertheless, little has been done in this area. In this connection, this paper presents a case study to showcase how social media data can be exploited. A structured approach is proposed which involves the analysis of social media comments and a statistical cluster analysis to identify the inter-relationships among important factors.
URI: http://hdl.handle.net/10397/55514
ISBN: 9781479964109
ISSN: 2157-3611
DOI: 10.1109/IEEM.2014.7058707
Appears in Collections:Conference Paper

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