Journal of Information Technology Applications and Management

  • 제30권4호
  • /
  • Pages.77-86
  • /
  • 2023
  • /
  • 1598-6284(pISSN)
  • /
  • 2508-1209(eISSN)
한국데이터전략학회 (Korea Data Strategy Society)
권호 표지
DOI QR코드

DOI QR Code

홍콩 영화에 관한 고객 리뷰의 텍스트 마이닝 기반 분석: 관객 선호도의 진화 발견

Text Mining-Based Analysis of Customer Reviews in Hong Kong Cinema: Uncovering the Evolution of Audience Preferences

  • Huayang Sun (Department of Health Management, Jinzhou Medical University) ;
  • Jung Seung Lee (School of Business, Hoseo University)
  • 투고 : 2023.02.10
  • 심사 : 2023.08.26
  • 발행 : 2023.08.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

This study conducted sentiment analysis on Hong Kong cinema from two distinct eras, pre-2000 and post-2000, examining audience preferences by comparing keywords from movie reviews. Before 2000, positive keywords like 'actors,' 'performance,' and 'atmosphere' revealed the importance of actors' popularity and their performances, while negative keywords such as 'forced' and 'violence' pointed out narrative issues. In contrast, post-2000 cinema emphasized keywords like 'scale,' 'drama,' and 'Yang Yang,' highlighting production scale and engaging narratives as key factors. Negative keywords included 'story,' 'cheesy,' 'acting,' and 'budget,' indicating challenges in storytelling and content quality. Word2Vec analysis further highlighted differences in acting quality and emotional engagement. Pre-2000 cinema focused on 'elegance' and 'excellence' in acting, while post-2000 cinema leaned towards 'tediousness' and 'awkwardness.' In summary, this research underscores the importance of actors, storytelling, and audience empathy in Hong Kong cinema's success. The industry has evolved, with a shift from actors to production quality. These findings have implications for the broader Chinese film industry, emphasizing the need for engaging narratives and quality acting to thrive in evolving cinematic landscapes.

키워드

참고문헌

  1. Ahn, J. A., "China's Post '80s and '90s Generation and Selective Reception of Foreign Popular Culture: The Case of Beijing", Journal of the Korea Contents Association, Vol. 14, No. 6, 2014, pp.34-43. https://doi.org/10.5392/JKCA.2014.14.06.034
  2. Chen, J., Chen, X., Chao, X. and Wang, H., "How electronic word of mouth dynamically influences product sales and supplies: An evidence from China film industry", Economic research-Ekonomska istrazivanja, Vol. 35, No. 1, 2022, pp. 637-656. https://doi.org/10.1080/1331677X.2021.1931910
  3. Church, K. W., "Word2Vec", Natural Language Engineering, Vol. 23, No. 1, 2017, pp. 155-162. https://doi.org/10.1017/S1351324916000334
  4. Fay, C., "Text Mining with R: A Tidy Approach", Journal of Statistical Software, Vol. 83, 2018, pp. 1-3. https://doi.org/10.18637/jss.v083.b01
  5. Jeon, H. S., "Research on Movie Reviews and Ratings Through Big Data - Focused on Relationship Between Major Features of Movie Reviews and the Box Office Outcomes", Consumer Culture Research, Vol. 25, No. 1, 2022, pp. 1-26.
  6. Lardy, N. R., Markets over Mao: The rise of private business in China, Columbia University Press, 2014.
  7. Lin, C. and He, Y., "Joint sentiment/topic model for sentiment analysis", In Proceedings of the 18th ACM conference on Information and knowledge management, November 2009, pp. 375-384.
  8. Mcauliffe, J. and Blei, D., "Supervised topic models", Advances in Neural InFormation Processing Systems, 2007, p.20.
  9. Qaiser, S. and Ali, R., "Text mining: use of TF-IDF to examine the relevance of words to documents", International Journal of Computer Applications, Vol. 181, No. 1, 2018, pp. 25-29. https://doi.org/10.5120/ijca2018917395
  10. Rahman, A. and Hossen, M. S., "Sentiment analysis on movie review data using machine learning approach", The Proceedings of 2019 international conference on bangla speech and language processing (ICBSLP), September, 2019, pp. 1-4.
  11. Xu, A., Qi, T., and Dong, X., "Analysis of the Douban online review of the MCU: based on LDA topic model", Journal of Physics: Conference Series, Vol. 1437, No. 1, 2020, p. 012102.
  12. Yessenov, K. and Misailovic, S., "Sentiment analysis of movie review comments", Methodology, Vol. 17, 2009, pp. 1-7.