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  • P-ISSN1013-0799
  • E-ISSN2586-2073
  • KCI

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Vol.35 No.1

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A Study on the Deduction of Social Issues Applying Word Embedding: With an Empasis on News Articles related to the Disables

Journal of the Korean Society for Information Management / Journal of the Korean Society for Information Management, (P)1013-0799; (E)2586-2073
2018, v.35 no.1, pp.231-250
https://doi.org/10.3743/KOSIM.2018.35.1.231


& (2018). A Study on the Deduction of Social Issues Applying Word Embedding: With an Empasis on News Articles related to the Disables. Journal of the Korean Society for Information Management, 35(1), 231-250, https://doi.org/10.3743/KOSIM.2018.35.1.231

Abstract

In this paper, we propose a new methodology for extracting and formalizing subjective topics at a specific time using a set of keywords extracted automatically from online news articles. To do this, we first extracted a set of keywords by applying TF-IDF methods selected by a series of comparative experiments on various statistical weighting schemes that can measure the importance of individual words in a large set of texts. In order to effectively calculate the semantic relation between extracted keywords, a set of word embedding vectors was constructed by using about 1,000,000 news articles collected separately. Individual keywords extracted were quantified in the form of numerical vectors and clustered by K-means algorithm. As a result of qualitative in-depth analysis of each keyword cluster finally obtained, we witnessed that most of the clusters were evaluated as appropriate topics with sufficient semantic concentration for us to easily assign labels to them.

keywords
keyword extraction, clustering, topic modeling, word embedding, TF-IDF, 키워드 추출, 클러스터링, 토픽 모델링, 단어 임베딩, TF-IDF

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