KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Text Mining and Sentiment Analysis for Predicting Box Office Success

  • Kim, Yoosin (Big Data Analytics Department, University of Seoul) ;
  • Kang, Mingon (Department of Computer Science, Kennesaw State University) ;
  • Jeong, Seung Ryul (Business IT Graduate School, Kookmin University)
  • Received : 2018.03.05
  • Accepted : 2018.04.12
  • Published : 2018.08.31
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Abstract

After emerging online communications, text mining and sentiment analysis has been frequently applied into analyzing electronic word-of-mouth. This study aims to develop a domain-specific lexicon of sentiment analysis to predict box office success in Korea film market and validate the feasibility of the lexicon. Natural language processing, a machine learning algorithm, and a lexicon-based sentiment classification method are employed. To create a movie domain sentiment lexicon, 233,631 reviews of 147 movies with popularity ratings is collected by a XML crawling package in R program. We accomplished 81.69% accuracy in sentiment classification by the Korean sentiment dictionary including 706 negative words and 617 positive words. The result showed a stronger positive relationship with box office success and consumers' sentiment as well as a significant positive effect in the linear regression for the predicting model. In addition, it reveals emotion in the user-generated content can be a more accurate clue to predict business success.

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