Noise removal using TF-IDF criterion for extracting patent keyword

Jongchan Kim, Dohan Choe, Gabjo Kim, Sangsung Park, Dongsik Jang

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


These days, governments and enterprises are analyzing trends in technology as a part of their investment strategy and R&D planning. Qualitative methods by experts are mainly used in technology trend analyses. However, such methods are inefficient in terms of cost and time for large amounts of data. In this study, we quantitatively analyzed patent data using text mining with TF-IDF used as weights. Keywords and noises were also classified using TF-IDF weighting. In addition, we propose new criteria for removing noises more effectively, and visualize the resulting keywords derived from patent data using social network analysis (SNA).

Original languageEnglish
Pages (from-to)61-69
Number of pages9
JournalAdvances in Intelligent Systems and Computing
Publication statusPublished - 2014


  • Extraction
  • Patent analysis
  • TF-IDF
  • Text mining

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)


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