Forecasting box office revenue of movies with BP neural network

Zhang, Li; Luo, Jianhua; Yang, Shuai

doi:10.1016/j.eswa.2008.07.064

Cited by 118 publications

(63 citation statements)

References 34 publications

(33 reference statements)

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“…Most previous studies predicted the movie gross income based on structured IMDB data analysis of specific characteristics [42], [43], [6], e.g., the number of one-week-old theaters, the rating from the Motion Picture Association of America, director, main actors, movie's genre, budget, and so on, but with somewhat limited success. Nevertheless, recent work [2], [12], [31] has shown the power of social media in predicting financial market phenomenon such as stock price movement, product sales, and financial risk.…”

Section: In the Usmentioning

confidence: 99%

“…Given that movie box-office revenue is a direct profit of the film industry, it is an important indicator for measuring the success of a movie [29], [32]. The accurate prediction of movie box-office revenues is highly significant for the reduction of market risk, improvement of the management of the film industry, and promotion of the development of a film-related derivative product market [42], [43]. However, predicting movie box-office revenues is a challenging problem, as it is very difficult to discover the essential reason for the volatility of the movie box-office revenue [29].…”

Section: Introductionmentioning

confidence: 99%

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Predicting movie Box-office revenues by exploiting large-scale social media content

Liu

Ding

Chen

et al. 2014

Multimed Tools Appl

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Predicting the box-office revenue of a movie before its theatrical release is an important but challenging problem that requires a high level of Artificial Intelligence. Nowadays, social media has shown its predictive power in various domains, which motivates us to exploit social media content to predict box-office revenues. In this study, we employ both linear and non-linear regression models, which are based on the crowd wisdom of social media, especially the posts of users, to predict movie box-office revenues. More specifically, the attention and popularity of the movie, purchase intention of users, and comments of users are automatically mined from social media data. In our model, the use of Linear Regression and Support Vector Regression in predicting the boxoffice revenue of a movie before its theatrical release is explored. To evaluate the effectiveness of the proposed approach, a cross-validation experiment is conducted. The experimental results show that large-scale social media content is correlated with movie box-office revenues and that the purchase intention of users can lead to more accurate movie box-office revenue predictions. Both the linear and non-linear prediction models have the advantage of predicting movie grosses in our experiments.

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Section: In the Usmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting movie Box-office revenues by exploiting large-scale social media content

Liu

Ding

Chen

et al. 2014

Multimed Tools Appl

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“…Due to its excellent ability of non-linear pattern recognition, generalization, self-organization and selflearning, the Artificial Neural Network Approach (ANNA) has been proved to be of widespread utility in engineering and is steadily advancing into diverse areas as material sciences (Li et al 2006), voice recognition, loan-risk assessment, stock market analysis, box office revenue forecasting (Zhang et al 2009) and military target discrimination. In geosciences and geo-engineering, neural networks have been applied in rock mechanics and rock engineering (Zhang et al 1991;Ghaboussi 1992;Lee and Sterling 1992), soil engineering (Kung et al 2007), well-log and well-test interpretation (Rogers et al 1992;AlKaabl and Lee 1993), seismic and satellite image processing (de Groot 1993;Penn et al 1993), groundwater characterization and remediation (Rizzo and Doughery 1994;Rogers and Dowla 1994), earthquake intensity prediction (Tung et al 1994), oil reservoir prediction (Yu et al 2008) and conductive fracture identification (Thomas and La Pointe 1995).…”

Section: Introductionmentioning

confidence: 99%

New Prediction Models for Mean Particle Size in Rock Blast Fragmentation

Kulatilake

Hudaverdi

2012

Geotech Geol Eng

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The paper refers the reader to a blast data base developed in a previous study. The data base consists of blast design parameters, explosive parameters, modulus of elasticity and in situ block size. A hierarchical cluster analysis was used to separate the blast data into two different groups of similarity based on the intact rock stiffness. The group memberships were confirmed by the discriminant analysis. A part of this blast data was used to train a single-hidden layer back propagation neural network model to predict mean particle size resulting from blast fragmentation for each of the obtained similarity groups. The mean particle size was considered to be a function of seven independent parameters. An extensive analysis was performed to estimate the optimum value for the number of units for the hidden layer for each of the obtained similarity groups. The blast data that were not used for training were used to validate the trained neural network models. For the same two similarity groups, multivariate regression models were also developed to predict mean particle size. Capability of the developed neural network models as well as multivariate regression models was determined by comparing predictions with measured mean particle size values and predictions based on one of the most applied fragmentation prediction models appearing in the blasting literature. Prediction capability of the trained neural network models as well as multivariate regression models was found to be strong and better than the existing most applied fragmentation prediction model. Diversity of the blasts data used is one of the most important aspects of the developed models.

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“…Therefore, BP neural network is the core part of feed-forward neural networks, and is the most essential artificial neural network [3] .That anyone of continuous functions in a closed interval can be approximated by a BP network with one hidden layer has been proved by Robert Hecht-Nielson in 1989, thus any n-dimension to mdimensional mapping can be completed with a threelayer BP network [4] . In this paper, the structure of the BP neural network with the LM algorithm as the learning algorithm is the same as the standard BP network (see Fig.2).…”

Section: Breakout Prediction Based On Bp Neural Network Of Lm Algorithmmentioning

confidence: 99%

Breakout Prediction Based on BP Neural Network in Continuous Casting Process

Zhang

Fan

2016

MATEC Web of Conferences

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Abstract.An improved BP neural network model was presented by modifying the learning algorithm of the traditional BP neural network, based on the Levenberg-Marquardt algorithm, and was applied to the breakout prediction system in the continuous casting process. The results showed that the accuracy rate of the model for the temperature pattern of sticking breakout was 96.43%, and the quote rate was 100%, that verified the feasibility of the model.

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Forecasting box office revenue of movies with BP neural network

Cited by 118 publications

References 34 publications

Predicting movie Box-office revenues by exploiting large-scale social media content

Predicting movie Box-office revenues by exploiting large-scale social media content

New Prediction Models for Mean Particle Size in Rock Blast Fragmentation

Breakout Prediction Based on BP Neural Network in Continuous Casting Process

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