Movie Title Keywords: A Text Mining and Exploratory Factor Analysis of Popular Movies in the United States and China

Xingyao Xiao, Yihong Cheng and Jong-Min Kim
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Xingyao Xiao: Graduate School of Education, University of California, Berkeley, CA 94720, USA
Yihong Cheng: Lynch School of Education and Human Development, Boston College, Chestnut Hill, MA 02467, USA
Jong-Min Kim: Statistics Discipline, Division of Science and Mathematics, University of Minnesota-Morris, Morris, MN 56267, USA

JRFM, 2021, vol. 14, issue 2, 1-19

Abstract: Unprecedented opportunities have been brought by advancements in machine learning in the prediction of the economic success of movies. The analysis of movie title keywords is one promising but rarely investigated direction of study. To address this gap, we performed a text mining and exploratory factor analysis (EFA) of the relationships between movie titles and their corresponding movies’ levels of success. Specifically, intragroup and intergroup analyses of 217 top hit movies in the United States and 245 top hit movies in China showed that successful movies in these two major movie markets with outstanding total lifetime grosses featured titles with similar and different patterns of most frequently used words, revealing useful information about viewers’ preferences in these countries. The findings of this study will serve to better inform the movie industry in giving more economically promising names to their products from a machine-learning perspective and inspire further studies.

Keywords: text mining; exploratory factor analysis (EFA); movie title keywords (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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