A case study on mining social media data

H. K. Chan, E. Lacka, Wai Yee Yee, M. K. Lim

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationIEEM 2014 - 2014 IEEE International Conference on Industrial Engineering and Engineering Management
PublisherIEEE Computer Society
Pages593-596
Number of pages4
Volume2015-January
ISBN (Electronic)9781479964109
DOIs
Publication statusPublished - 1 Jan 2014
Event2014 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2014 - Selangor, Malaysia
Duration: 9 Dec 201412 Dec 2014

Conference

Conference2014 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2014
CountryMalaysia
CitySelangor
Period9/12/1412/12/14

Keywords

  • cluster analysis
  • content analysis
  • Social Media
  • text mining

ASJC Scopus subject areas

  • Business, Management and Accounting (miscellaneous)
  • Industrial and Manufacturing Engineering
  • Safety, Risk, Reliability and Quality

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