Noise removal using TF-IDF criterion for extracting patent keyword

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

doi:10.1007/978-3-319-05527-5_7

Noise removal using TF-IDF criterion for extracting patent keyword

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

School of Industrial Management Engineering

Research output: Contribution to journal › Article › peer-review

5 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 language	English
Pages (from-to)	61-69
Number of pages	9
Journal	Advances in Intelligent Systems and Computing
Volume	271
DOIs	https://doi.org/10.1007/978-3-319-05527-5_7
Publication status	Published - 2014

Keywords

Extraction
Patent analysis
TF-IDF
Text mining

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science(all)

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10.1007/978-3-319-05527-5_7

Cite this

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title = "Noise removal using TF-IDF criterion for extracting patent keyword",

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).",

keywords = "Extraction, Patent analysis, TF-IDF, Text mining",

author = "Jongchan Kim and Dohan Choe and Gabjo Kim and Sangsung Park and Dongsik Jang",

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year = "2014",

doi = "10.1007/978-3-319-05527-5_7",

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AU - Kim, Jongchan

AU - Choe, Dohan

AU - Kim, Gabjo

AU - Park, Sangsung

AU - Jang, Dongsik

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2014.

PY - 2014

Y1 - 2014

N2 - 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).

AB - 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).

KW - Extraction

KW - Patent analysis

KW - TF-IDF

KW - Text mining

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U2 - 10.1007/978-3-319-05527-5_7

DO - 10.1007/978-3-319-05527-5_7

M3 - Article

AN - SCOPUS:84927618498

SN - 2194-5357

VL - 271

SP - 61

EP - 69

JO - Advances in Intelligent and Soft Computing

JF - Advances in Intelligent and Soft Computing

ER -

Noise removal using TF-IDF criterion for extracting patent keyword

Abstract

Keywords

ASJC Scopus subject areas

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