A Study on the Deduction of Social Issues Applying Word Embedding: With an Empasis on News Articles related to the Disables (단어 임베딩(Word Embedding) 기법을 적용한 키워드 중심의 사회적 이슈 도출 연구: 장애인 관련 뉴스 기사를 중심으로)

Garam Choi (최가람); Sung-Pil Choi (최성필)

doi:10.3743/KOSIM.2018.35.1.231

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
Abbr : JKOSIM
2018, 35(1), pp.231~250
DOI : 10.3743/KOSIM.2018.35.1.231
Publisher : 한국정보관리학회
Research Area : Interdisciplinary Studies > Library and Information Science
Received : March 8, 2018
Accepted : March 22, 2018
Published : March 30, 2018

Garam Choi ¹, Sung-Pil Choi ¹

¹경기대학교

Accredited

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

Citation status

* References for papers published after 2023 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

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Journal Central

Journal of the Korean Society for Information Management 2023 KCI Impact Factor : 1.31

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

ABSTRACT

KEYWORDS

Citation status

* References for papers published after 2023 are currently being built.

Journal of the Korean Society for Information Management 2023 KCI Impact Factor : 1.31

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

ABSTRACT

KEYWORDS

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Citation status

KCI Citation Counts (11)

REFERENCES (17) * References for papers published after 2023 are currently being built.

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* References for papers published after 2023 are currently being built.