Journal of Korea Game Society (한국게임학회 논문지)

Korea Game Society (한국게임학회)
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A Study on Automatic Comment Generation Using Deep Learning

딥 러닝을 이용한 자동 댓글 생성에 관한 연구

  • Choi, Jae-yong (Dept. of Game & Multimedia Engineering, Korea Polytechnic University) ;
  • Sung, So-yun (Dept. of Game & Multimedia Engineering, Korea Polytechnic University) ;
  • Kim, Kyoung-chul (Dept. of Game & Multimedia Engineering, Korea Polytechnic University)
  • 최재용 (한국산업기술대학교 게임공학과) ;
  • 성소윤 (한국산업기술대학교 게임공학과) ;
  • 김경철 (한국산업기술대학교 게임공학과)
  • Received : 2018.09.10
  • Accepted : 2018.10.08
  • Published : 2018.10.20
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Abstract

Many studies in deep learning show results as good as human's decision in various fields. And importance of activation of online-community and SNS grows up in game industry. Even it decides whether a game can be successful or not. The purpose of this study is to construct a system which can read texts and create comments according to schedule in online-community and SNS using deep learning. Using recurrent neural network, we constructed models generating a comment and a schedule of writing comments, and made program choosing a news title and uploading the comment at twitter in calculated time automatically. This study can be applied to activating an online game community, a Q&A service, etc.

최근 다수의 분야에서 딥 러닝을 통한 연구 성과들이 사람의 판단력에 근접하는 결과를 보여주고 있다. 그리고 게임 산업에서는 온라인 커뮤니티, SNS의 활성화가 게임 흥행 여부를 결정할 정도로 중요성이 높아지고 있다. 본 연구는 딥 러닝을 이용해 온라인 커뮤니티, SNS에서 활동할 수 있는 시스템을 구성하고, 온라인 공간에서 사람들이 작성한 텍스트를 읽고 그에 대한 반응을 생성하고 스케쥴에 따라 트위터에 올리는 것을 목표로 한다. 순환 신경망(Recurrent Neural Network)을 이용해 텍스트를 생성하고 글 작성 스케쥴을 생성하는 모델들을 구성했고, 생성한 시각에 맞춰 모델들에 뉴스 제목을 입력해 댓글을 출력 받고 트위터에 작성하는 프로그램을 구현했다. 본 연구결과는 온라인 게임 커뮤니티 활성화, Q&A 서비스 등에 적용이 가능할 것으로 예상된다.

Keywords

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