Analyzing unstructured Facebook social network data through web text mining: A study of online shopping firms in Turkey

Kahya-Ozyirmidokuz E.

INFORMATION DEVELOPMENT, vol.32, no.1, pp.70-80, 2016 (SSCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 32 Issue: 1
  • Publication Date: 2016
  • Doi Number: 10.1177/0266666914528523
  • Journal Indexes: Social Sciences Citation Index (SSCI), Scopus
  • Page Numbers: pp.70-80
  • Keywords: text mining, web mining, Facebook, knowledge discovery in databases, data mining, information extraction, online shopping, Turkey, EXTRACTION
  • Kayseri University Affiliated: No


The large amounts of Facebook social network data which are generated and collected need to be analyzed for valuable decision making information about shopping firms in Turkey. In addition, analyzing social network data from outside the firms becomes a critical business need for the firms which actively use Facebook. To have a competitive advantage, firms must translate social media texts into something more quantitative to extract information. In this study, web text mining techniques are used to determine popular online shopping firms' Facebook patterns. For this purpose, 200 popular Turkish companies' web URLs are used. Web text mining through natural language processing techniques is examined. Similarity analysis and clustering are done. Consequently, the clusters of the Facebook websites and their relationships and similarities of the firms are obtained.