토픽 레이블링을 위한 토픽 키워드 산출 방법
A Method of Calculating Topic Keywords for Topic Labeling
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참고문헌
키워드
초록
Topics calculated using LDA topic modeling have to be labeled separately. When labeling a topic, we look at the words that represent the topic, and label the topic. Therefore, it is important to first make a good set of words that represent the topic. This paper proposes a method of calculating a set of words representing a topic using TextRank, which extracts the keywords of a document. The proposed method uses Relevance to select words related to the topic with discrimination. It extracts topic keywords using the TextRank algorithm and connects keywords with a high frequency of simultaneous occurrence to express the topic with a higher coverage.
Abstract
Topics calculated using LDA topic modeling have to be labeled separately. When labeling a topic, we look at the words that represent the topic, and label the topic. Therefore, it is important to first make a good set of words that represent the topic. This paper proposes a method of calculating a set of words representing a topic using TextRank, which extracts the keywords of a document. The proposed method uses Relevance to select words related to the topic with discrimination. It extracts topic keywords using the TextRank algorithm and connects keywords with a high frequency of simultaneous occurrence to express the topic with a higher coverage.
참고문헌 (13)
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- 김창식, "머신러닝 및 딥러닝 연구동향 분석: 토픽모델링을 중심으로", (사)디지털산업정보학회 논문지, 15(2), pp.19~28 (2019), (사)디지털산업정보학회
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