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http://dx.doi.org/10.13088/jiis.2016.22.3.071

A Study on Analyzing Sentiments on Movie Reviews by Multi-Level Sentiment Classifier  

Kim, Yuyoung (Graduate School of Library and Information Science, Yonsei University)
Song, Min (Department of Library and Information Science, Yonsei University)
Publication Information
Journal of Intelligence and Information Systems / v.22, no.3, 2016 , pp. 71-89 More about this Journal
Abstract
Sentiment analysis is used for identifying emotions or sentiments embedded in the user generated data such as customer reviews from blogs, social network services, and so on. Various research fields such as computer science and business management can take advantage of this feature to analyze customer-generated opinions. In previous studies, the star rating of a review is regarded as the same as sentiment embedded in the text. However, it does not always correspond to the sentiment polarity. Due to this supposition, previous studies have some limitations in their accuracy. To solve this issue, the present study uses a supervised sentiment classification model to measure a more accurate sentiment polarity. This study aims to propose an advanced sentiment classifier and to discover the correlation between movie reviews and box-office success. The advanced sentiment classifier is based on two supervised machine learning techniques, the Support Vector Machines (SVM) and Feedforward Neural Network (FNN). The sentiment scores of the movie reviews are measured by the sentiment classifier and are analyzed by statistical correlations between movie reviews and box-office success. Movie reviews are collected along with a star-rate. The dataset used in this study consists of 1,258,538 reviews from 175 films gathered from Naver Movie website (movie.naver.com). The results show that the proposed sentiment classifier outperforms Naive Bayes (NB) classifier as its accuracy is about 6% higher than NB. Furthermore, the results indicate that there are positive correlations between the star-rate and the number of audiences, which can be regarded as the box-office success of a movie. The study also shows that there is the mild, positive correlation between the sentiment scores estimated by the classifier and the number of audiences. To verify the applicability of the sentiment scores, an independent sample t-test was conducted. For this, the movies were divided into two groups using the average of sentiment scores. The two groups are significantly different in terms of the star-rated scores.
Keywords
sentiment analysis; sentiment classification; movie review analysis; text mining;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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1 Kim, S. H. and J. M. Han, "An Analysis of Motion Picture Box Office Performance : Focusing on Korean Movies Released in 2012," Social Science Studies, Vol.53, No.1 (2014), 191-214.
2 Konig, A. C. and E. Brill, "Reducing the human overhead in text categorization," Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2006), 598-603.
3 Krauss, J., S. Nann, D. Simon, P. A. Gloor and K. Fischbach, "Predicting Movie Success and Academy Awards through Sentiment and Social Network Analysis," In ECIS, (2008), 2026-2037.
4 Lee, K. J. and W. J. Jang, "Predicting Financial Success of a Movie Using Bayesian Choice Model," Proceedings of the Korean Operations and Management Science Society Conference, (2006), 1428-1433.
5 Liu, B., "Sentiment analysis and opinion mining," Synthesis Lectures on Human Language Technologies, Vol.5, No.1(2012), 1-167.   DOI
6 Melville, P., W. Gryc and R. D. Lawrence, "Sentiment analysis of blogs by combining lexical knowledge with text classification," Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, (2009), 1275-1284.
7 Mullen, T. and N. Collier, "Sentiment analysis using support vector machines with diverse information sources," Proceedings of Empirical Methods in Natural Language Processing, (2004), 412-418.
8 Neelamegham, R. and P. Chintagunta, "A Bayesian model to forecast new product performance in domestic and international markets," Marketing Science, Vol.18, No.2(1999), 115-136.   DOI
9 Oh, Y.-J. and S.-H. Chae, "Movie Rating Inference by Construction of Movie Sentiment Sentence using Movie comments and ratings," Journal of Internet Computing and Services, Vol.16, No.2(2015), 41-28.   DOI
10 Pagano, D. and W. Maalej, "User feedback in the appstore: An empirical study," Proceedings of Requirements Engineering Conference, 2013 21st IEEE International, (2013), 125-134.
11 Pak, A. and P. Paroubek, "Twitter as a corpus for sentiment analysis and opinion mining," Proceedings of the Seventh International Conference on Language Resources and Evaluation, (2010), 1320-1326.
12 Pang, B., L. Lee and S. Vaithyanathan, "Thumbs up?: sentiment classification using machine learning techniques," Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, (2002), 79-86.
13 Pang, B. and L. Lee, "Opinion mining and sentiment analysis," Foundations and Trends in Information Retrieval, Vol.2, No.1-2 (2008), 1-135.   DOI
14 Park, S. H. and H.-J. Song, "Word of Mouth and Box Office Performance: WOM's Impact on Weekly Box Office Revenues," Korean Journal of Journalism and Communication Studies, Vol.56, No.4(2012), 210-235.
15 Park, S. H., H.-J. Song and W.-K. Jung, "The Determinants of Motion Picture Box Office Performance : Evidence from Korean Movies Released in 2009-2010," Journal of Communication Science, Vol.11, No.4(2011), 231-258.
16 Wyatt, J., "High concept, product differentiation, and the contemporary US film industry," Current Research in Film: Audiences, Economics and Law, Vol.5(1991), 86-105.
17 Sarvabhotla, K., P. Pingali and V. Varma, "Supervised learning approaches for rating customer reviews," Journal of Intelligent Systems, Vol.19, No.1(2010), 79-94.
18 Seroussi, Y., F. Bohnert and I. Zukerman, "Personalised rating prediction for new users using latent factor models," Proceedings of the 22nd ACM conference on Hypertext and hypermedia, (2011), 47-56.
19 Shin, J. and H. Kim, "A Robust Pattern-based Feature Extraction Method for Sentiment Categorization of Korean Customer Reviews," Journal of KIISE : Software and Applications, Vol.37, No.12(2010), 946-950.
20 Yanagimoto, H., M. Shimada and A. Yoshimura, "Document similarity estimation for sentiment analysis using neural network". Proceedings of Computer and Information Science (ICIS), 2013 IEEE/ACIS 12th International Conference, (2013), 105-110.
21 Chevalier, J. A. and D. Mayzlin, "The effect of word of mouth on sales: Online book reviews," Journal of Marketing Research, Vol.43, No.3(2006), 345-354.   DOI
22 Annett, M. and G. Kondrak, "A comparison of sentiment analysis techniques: Polarizing movie blogs," Advances in Artificial Intelligence, (2008), 25-35.
23 Appel, O., F. Chiclana and J. Carter, "Main concepts, state of the art and future research questions in sentiment analysis," Acta Polytechnica Hungarica, Vol.12, No.3(2015), 87-108.
24 Asur, S. and B. A. Huberman, "Predicting the future with social media. In Web Intelligence and Intelligent Agent Technology (WI-IAT)," Proceedings of 2010 IEEE/WIC/ACM International Conference, (2010), 492-299.
25 Chen, Y. and J. Xie, 'Online consumer review: Word-of-mouth as a new element of marketing communication mix," Management Science, Vol.54, No.3(2008), 477-491.   DOI
26 Chen, H. and D. Zimbra, "AI and opinion mining," IEEE Intelligent Systems, Vol.25, No.3(2010), 74-80.   DOI
27 Cui, G., H. K. Lui and X. Guo, "The effect of online consumer reviews on new product sales," International Journal of Electronic Commerce, Vol.17, No.1(2012), 39-58.   DOI
28 Dellarocas, C., N. Awad and M. Zhang, "Using online ratings as a proxy of word-of-mouth in motion picture revenue forecasting," Smith School of Business, University of Maryland, 2005.
29 Ferguson, P., N. O'Hare, M. Davy, A. Bermingham, P. Sheridan, C. Gurrin and A. F. Smeaton, "Exploring the use of paragraph-level annotations for sentiment analysis of financial blogs," Proceedings of WOMAS 2009-Workshop on Opinion Mining and Sentiment Analysis, (2009).
30 Duan, W. and A. B. Whinston, "The dynamics of online word-of-mouth and product sales-An empirical investigation of the movie industry," Journal of Retailing, Vol.84, No.2(2008), 233-242.   DOI
31 Ghose, Al, and P. G. Ipeirotis, "Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics," Knowledge and Data Engineering, IEEE Transactions on, Vol.23, No.10(2011), 1498-1512.   DOI
32 Glorot, X., A. Bordes and Y. Bengio, "Domain adaptation for large-scale sentiment classification: A deep learning approach," Proceedings of the 28th International Conference on Machine Learning, (2011), 513-520.
33 Hu, Z., W. Ding and X. Zheng, "Review sentiment analysis based on deep learning," Proceedings of e-Business Engineering (ICEBE) 2015 IEEE 12th International Conference, (2015), 87-94.
34 Heo, M. H., P. S. Kang and S. Cho, "Predicting Box-office with Opinion mining reviews," Proceedings of the Korean Operations and Management Science Society Conference, (2013), 487-500.
35 Airoldi, E., X. Bai and R. Padman, "Markov blankets and meta-heuristics search: sentiment extraction from unstructured texts," Proceedings of International Workshop on Knowledge Discovery on the Web, (2004), 167-187.
36 Amplayo, R. K. and J. Occidental, "Multi-level classifier for the detection of insults in social media," Proceedings of 15th Philippine Computing Science Congress, (2015).
37 Kennedy, A. and D. Inkpen, "Sentiment classification of movie reviews using contextual valence shifters," Computational intelligence, Vol.22, No.2(2006), 110-125.   DOI
38 Jakob, N., S. H. Weber, M. C. Muller and I. Gurevych, "Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations," Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion, (2009), 57-64.
39 Jo, S. Y., H.-K. Kim, B. Kim and H.-W. Kim, "Predicting Movie Revenue by Online Review Mining : Using the Opening Week Online Review," Information Systems Review, Vol.16, No.3(2014), 113-134.
40 Jung, Y., Research in Information Retrieval, revised edition, Yonsei University Press, 2012.
41 Kim, M. H., S. E. Kim and Y. J. Choi, "The Determinants of Box-office Performance of Korean Films and Implications for Policies," Film Studies, No.46(2010), 31-56.
42 Kim, Y. H. and J. H. Hong, "A Study for the Development of Motion Picture Box-office Prediction Model," Communications for Statistical Applications and Methods, Vol.18, No.6(2011), 859-869.   DOI