Journal of Internet Computing and Services (인터넷정보학회논문지)

Korean Society for Internet Information (한국인터넷정보학회)
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Comparative Study of User Reactions in OTT Service Platforms Using Text Mining

텍스트 마이닝을 활용한 OTT 서비스 플랫폼별 사용자 반응 비교 연구

  • Soonchan Kwon (Graduate School of Information, Yonsei University) ;
  • Jieun Kim (Graduate School of Information, Yonsei University) ;
  • Beakcheol Jang (Graduate School of Information, Yonsei University)
  • 권순찬 ;
  • 김지은 ;
  • 장백철
  • Received : 2024.01.16
  • Accepted : 2024.05.29
  • Published : 2024.06.30
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Abstract

This study employs text mining techniques to compare user responses across various Over-The-Top (OTT) service platforms. The primary objective of the research is to understand user satisfaction with OTT service platforms and contribute to the formulation of more effective review strategies. The key questions addressed in this study involve identifying prominent topics and keywords in user reviews of different OTT services and comprehending platform-specific user reactions. TF-IDF is utilized to extract significant words from positive and negative reviews, while BERTopic, an advanced topic modeling technique, is employed for a more nuanced and comprehensive analysis of intricate user reviews. The results from TF-IDF analysis reveal that positive app reviews exhibit a high frequency of content-related words, whereas negative reviews display a high frequency of words associated with potential issues during app usage. Through the utilization of BERTopic, we were able to extract keywords related to content diversity, app performance components, payment, and compatibility, by associating them with content attributes. This enabled us to verify that the distinguishing attributes of the platforms vary among themselves. The findings of this study offer significant insights into user behavior and preferences, which OTT service providers can leverage to improve user experience and satisfaction. We also anticipate that researchers exploring deep learning models will find our study results valuable for conducting analyses on user review text data.

본 연구는 텍스트 마이닝 기법을 활용하여 다양한 OTT(Over-The-Top) 서비스 플랫폼에 대한 사용자 반응을 비교한다. 연구의 주요 목표는 OTT 서비스 플랫폼의 사용자 만족도를 파악하여 보다 효과적인 리뷰 전략을 수립하는 데 기여하는 것이다. 본 연구에서 다루는 주요 질문에는 다양한 OTT 서비스에 대한 사용자 리뷰에서 두드러진 토픽과 키워드를 식별하고 플랫폼별 사용자 반응을 이해하는 것이 포함된다. 이를 위해 긍정, 부정 리뷰에서 중요 단어를 추출하기 위해 Tf-idf를, 복잡한 사용자 리뷰를 보다 정교하고 포괄적으로 분석하기 위해 고급 토픽 모델링 기법인 BERTopic을 사용한다. Tf-idf 분석한 결과, 앱에 대한 긍정 리뷰는 콘텐츠와 관련된 단어들의 수치가 높았으며 부정 리뷰에서는 앱 사용 과정에서 발생할 수 있는 문제점에 관한 단어 수치가 높게 기록되었다. BERTopic을 활용한 토픽 모델링에서는 콘텐츠의 속성과 연관 지어 콘텐츠의 다양성, 앱 성능 요소, 결제, 호환성에 관한 키워드를 도출하였으며, 플랫폼 별로 두각을 보이는 속성이 다르다는 점도 확인하였다. 본 연구 결과는 사용자 행동과 선호도에 대한 중요한 인사이트를 제공하며, 이를 통해 OTT 서비스 제공업체는 사용자 경험과 만족도를 개선하는 데 활용할 수 있다. 또한, 연구자들은 사용자 리뷰 텍스트 분석에서 딥러닝 모델을 활용한 연구의 아이디어를 얻을 수 있을 것이라 기대한다.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) funded by Korean Government under Grant RS-2023-00273751

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