TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Text Mining and Sentiment Analysis for Predicting Box Office Success |
| 영문제목(English Title) |
Text Mining and Sentiment Analysis for Predicting Box Office Success |
| 저자(Author) |
Yoosin Kim
Mingon Kang
Seung Ryul Jeong
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| 원문수록처(Citation) |
VOL 12 NO. 08 PP. 4090 ~ 4102 (2018. 08) |
한글내용
(Korean Abstract) |
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영문내용
(English 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 usergenerated content can be a more accurate clue to predict business success.
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| 키워드(Keyword) |
Text Mining; Sentiment Analysis
Prediction
Box office Success
Word of Mouth
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| 파일첨부 |
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