Inter-category map: Building cognition network of general customers through big data mining

Gil Young Song, Youngjoon Cheon, Kihwang Lee, Kyung Min Park, Hae Chang Rim

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Social media is considered a valuable platform for gathering and analyzing the collective and subconscious opinions of people in Internet and mobile environments, where they express, explicitly and implicitly, their daily preferences for brands and products. Extracting and tracking the various attitudes and concerns that people express through social media could enable us to categorize brands and decipher individuals' cognitive decision-making structure in their choice of brands. We investigate the cognitive network structure of consumers by building an inter-category map through the mining of big data. In so doing, we create an improved online recommendation model. Building on economic sociology theory, we suggest a framework for revealing collective preference by analyzing the patterns of brand names that users frequently mention in the online public sphere. We expect that our study will be useful for those conducting theoretical research on digital marketing strategies and doing practical work on branding strategies.

Original languageEnglish
Pages (from-to)583-600
Number of pages18
JournalKSII Transactions on Internet and Information Systems
Issue number2
Publication statusPublished - 2014 Feb 27


  • Big data mining
  • Brand choice
  • Inter-category map
  • Social media

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications


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