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

    6 Citations (Scopus)

    Abstract

    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
    Volume271
    DOIs
    Publication statusPublished - 2014

    Bibliographical note

    Publisher Copyright:
    © Springer International Publishing Switzerland 2014.

    Keywords

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

    ASJC Scopus subject areas

    • Control and Systems Engineering
    • General Computer Science

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