Browse > Article
http://dx.doi.org/10.7472/jksii.2021.22.5.47

A Study on Analysis of national R&D research trends for Artificial Intelligence using LDA topic modeling  

Yang, MyungSeok (Dept. of Data-Centric Problem Solving Research, KISTI)
Lee, SungHee (Div. NTIS, KISTI)
Park, KeunHee (Div. NTIS, KISTI)
Choi, KwangNam (Div. NTIS, KISTI)
Kim, TaeHyun (Div. NTIS, KISTI)
Publication Information
Journal of Internet Computing and Services / v.22, no.5, 2021 , pp. 47-55 More about this Journal
Abstract
Analysis of research trends in specific subject areas is performed by examining related topics and subject changes by using topic modeling techniques through keyword extraction for most of the literature information (paper, patents, etc.). Unlike existing research methods, this paper extracts topics related to the research topic using the LDA topic modeling technique for the project information of national R&D projects provided by the National Science and Technology Knowledge Information Service (NTIS) in the field of artificial intelligence. By analyzing these topics, this study aims to analyze research topics and investment directions for national R&D projects. NTIS provides a vast amount of national R&D information, from information on tasks carried out through national R&D projects to research results (thesis, patents, etc.) generated through research. In this paper, the search results were confirmed by performing artificial intelligence keywords and related classification searches in NTIS integrated search, and basic data was constructed by downloading the latest three-year project information. Using the LDA topic modeling library provided by Python, related topics and keywords were extracted and analyzed for basic data (research goals, research content, expected effects, keywords, etc.) to derive insights on the direction of research investment.
Keywords
topic modeling; Artificial Intelligence; National Research and Development Program; Research Trend; NTIS;
Citations & Related Records
연도 인용수 순위
  • Reference
1 MyungSeok Yang, WonKyun Joo, KiSeok Choi, YoungKuk Kim, YunJeong Kim, "Development of platform-based knowledge map service to get data insights of R&D institution on user-interested subjects", Wireless Personal Communications, 98(40: 3265-3285, 2018. https://doi.org/10.1007/s11277-017-5097-z   DOI
2 Namgyu Kim, Donghoon Lee, Hochang Choi, William Xiu Shun Wong, "Investigations on Techniques and Applications of Text Analytics", KICS, 42(2): 471-492, 2017. https://doi.org/10.7840/kics.2017.42.2.471   DOI
3 JunHyeong Park, Hyo-Jung Oh, "Comparison of Topic Modeling Methods for Analyzing Research Trends of Archives Management in Korea: focused on LDA and HDP", kliss, 48(4), 235-258, 2017. https://doi.org/10.16981/kliss.48.201712.235   DOI
4 Keon Chul Park, Chi Hyung Lee, "A Study on the Research Trends for Smart City using Topic Modeling", Journal of Internet Computing and Services(JICS), 20(3): 119-128, 2019. https://doi.org/10.7472/jksii.2019.20.3.119   DOI
5 Jin-myeong Chung Young-ho Park Woo-ju Kim, "Social Media Analysis Based on Keyword Related to Educational Policy Using Topic Modeling", Journal of Internet Computing and Services(JICS), 19(4): 53-63, 2018. https://doi.org/10.7472/jksii.2018.19.4.53   DOI
6 TaeHyun Kim, MyungSeok Yang, KwangNam Choi , "A Study on the Construction of the Terminology Dictionary for National R&D Information Utilization", Journal of Korea Contents Association, 19(10) :217-225, 2019. https://doi.org/10.5392/JKCA.2019.19.10.217   DOI
7 mecab, https://pypi.org/project/python-mecab-ko/
8 gensim, https://pypi.org/project/gensim/
9 Getting Started with Topic Modeling and MALLET, Shawn Graham, Scott Weingart, and Ian Milligan, https://programminghistorian.org/en/lessons/topic-modeling-and-mallet
10 ChunHo Nam, "Review of the applicability of topic modeling techniques in diary data research", Journal of Cross-Cultural Studies, 22(1):89-135. 2016.
11 NIA, "Artificial Inteligence in Society", 2019.