원문정보
Text Mining-Based Analysis of Customer Reviews in Hong Kong Cinema : Uncovering the Evolution of Audience Preferences
초록
영어
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.
목차
1. 서론
2. 이론적 배경
2.1 텍스트 마이닝
2.2 영화 리뷰 분석
3. 분석 방법론
3.1 분석 절차
3.2 텍스트 마이닝 방법론
4. 분석 결과
4.1 TF-IDF 분석 결과
4.2 Word2vec 분석 결과
5. 결론 및 향후 연구 방향
5.1 결론 및 시사점
5.2 향후 연구 방향
References
