Prediction of movies box office performance using social media

Apala, Krushikanth R.; Jose, M Eirin-Lopez; Chan, Chien-Chung; Liszka, Kathy J.; Gregorio, Federico de

doi:10.1145/2492517.2500232

Cited by 43 publications

(21 citation statements)

References 7 publications

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“…We first checked the way in which success is defined, because it is of paramount importance to the problem. As we mentioned earlier, past works have focused primarily on gross box office revenue [1,2,14,21,24,26], while some used the number of admissions [3,19]. The basic assumption for using the two as success metrics is simple-a movie that sells well at the box office is considered a success.…”

Section: The Definition Of Successmentioning

confidence: 99%

Early Predictions of Movie Success: The Who, What, and When of Profitability

Lash

Zhao

2016

Journal of Management Information Systems

126

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This paper proposes a decision support system to aid movie investment decisions at the early stage of movie productions. The system predicts the success of a movie based on its profitability by leveraging historical data from various sources. Using social network analysis and text mining techniques, the system automatically extracts several groups of features, including "who" are on the cast, "what" a movie is about, "when" a movie will be released, as well as "hybrid" features that match "who" with "what", and "when" with "what". Experiment results with movies during an 11-year period showed that the system outperforms benchmark methods by a large margin in predicting movie profitability. Novel features we proposed also made great contributions to the prediction. In addition to designing a decision support system with practical utilities, our analysis of key factors for movie profitability may also have implications for theoretical research on team performance and the success of creative work.

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Section: The Definition Of Successmentioning

confidence: 99%

Early Predictions of Movie Success: The Who, What, and When of Profitability

Lash

Zhao

2016

Journal of Management Information Systems

126

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“…After training and testing processes it has been seen that the developed sentiment analysis system has classified 433 of the 600 test tweets are correctly. So success rate of the system is found 72.33% which is an acceptable ratio while compared with other studies in the literature [5][6][7][8][9][10][11][12][13][14][15].…”

Section: E Classificationmentioning

confidence: 65%

“…These research areas range from analyzing public opinion about social and political issues, predicting election results, prediction of movie success to having idea about brand and product sales, learning company strategies and marketing preferences and even predicting success of mobile phone applications [5][6][7][8][9][10][11][12][13][14][15].…”

Section: Previous Studiesmentioning

confidence: 99%

“…In another study conducted by Apala et al [10], sentiment analysis was used for predicting movies box office performance using social networking data, too. In this context, historical data from IMDb movie database, followers count from Twitter and comments of Youtube viewers are collected.…”

Section: Previous Studiesmentioning

confidence: 99%

“…Sentiment analysis are generally used by companies in order for analyzing the opinion and feelings of the customers about products, services and company strategies. In addition, this analysis helps to celebrities like actor, actress and authors for understanding the attitudes and emotions of the public about themselves [5][6][7][8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Investigating Sentimental Relation between Social Media Presence and Academic Success of Turkish Universities

Gunduz

Demirhan

Sağıroğlu

2014

2014 13th International Conference on Machine Learning and Applications

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In this study an approach that uses social networking data for developing sentiment analysis system is proposed. With the help of developed software, it is tried to find out whether there is any relation between universities' academic success and sentiment of the public about them in social media. After collecting enough text based data from Twitter, preprocessing of data is carried out and final data is trained by means of Naïve Bayes Classifier. After testing process, experimental results have shown that developed sentiment analysis system can classify the tweets about top 10 universities according to URAP rankings in terms of their sentiment with the 72.33% success rate, and proposed methodology can be used by universities for understanding sentiment of the public about them in social media.

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Predicting Movies’ Box Office Result - A Large Scale Study Across Hollywood and Bollywood

Ruus

Sharma

2019

Complex Networks and Their Applications VIII

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Prediction of movies box office performance using social media

Cited by 43 publications

References 7 publications

Early Predictions of Movie Success: The Who, What, and When of Profitability

Early Predictions of Movie Success: The Who, What, and When of Profitability

Investigating Sentimental Relation between Social Media Presence and Academic Success of Turkish Universities

Predicting Movies’ Box Office Result - A Large Scale Study Across Hollywood and Bollywood

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