Topic Modeling with Deep Learning-based Sentiment Filters

감정 딥러닝 필터를 활용한 토픽 모델링 방법론

  • 최병설 (국민대학교 비즈니스 IT전문대학원) ;
  • 김남규 (국민대학교 경영정보학부)
  • Received : 2019.12.02
  • Accepted : 2019.12.18
  • Published : 2019.12.31


Purpose The purpose of this study is to propose a methodology to derive positive keywords and negative keywords through deep learning to classify reviews into positive reviews and negative ones, and then refine the results of topic modeling using these keywords. Design/methodology/approach In this study, we extracted topic keywords by performing LDA-based topic modeling. At the same time, we performed attention-based deep learning to identify positive and negative keywords. Finally, we refined the topic keywords using these keywords as filters. Findings We collected and analyzed about 6,000 English reviews of Gyeongbokgung, a representative tourist attraction in Korea, from Tripadvisor, a representative travel site. Experimental results show that the proposed methodology properly identifies positive and negative keywords describing major topics.


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