Pre-production box-office success quotient forecasting

Ahmed, Usman; Waqas, Humaira; Afzal, Muhammad Tanvır

doi:10.1007/s00500-019-04303-w

Cited by 24 publications

(19 citation statements)

References 29 publications

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“…Anantha Natarajan, Sai Harsha, and Santhosh Kumar (2019) develop a predictive analytics model for the box-office revenue using large datasets. Ahmed, Waqas, and Afzal (2019) look at the pre-production information usage to forecast future revenues. Cyclicity in the motion picture production is analyzed in Wallin (2019).…”

Section: Related Literaturementioning

confidence: 99%

Improving productivity in Hollywood with data science: Using emotional arcs of movies to drive product and service innovation in entertainment industries

Vecchio

Kharlamov

Parry

et al. 2020

Journal of the Operational Research Society

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Improving productivity in the entertainment industry is a very challenging task as it heavily depends on generating attractive content for the consumers. The consumer-centric design (putting the consumers at the centre of the content development and production) focuses on ways in which businesses can design customized services and products which accurately reflect consumer preferences. We propose a new framework which allows to use data science to optimize content-generation in entertainment and test this framework for the motion picture industry. We use the natural language processing methodology combined with econometric analysis to explore whether and to what extent emotions shape consumer preferences for media and entertainment content, which, in turn, affect revenue streams. By analyzing 6,174 movie scripts, we generate the emotional trajectory of each motion picture. We then combine the obtained mappings into clusters which represent groupings of consumer emotional journeys. These clusters are then plugged into an econometric model to predict overall success parameters of the movies including box office revenues, viewer satisfaction levels (captured by IMDb ratings), awards, as well as the number of viewers' and critics' reviews. We find that emotional arcs in movies can be partitioned into 6 basic shapes. The highest box offices are associated with the Man in a Hole shape which is characterized by an emotional fall followed by an emotional rise. This U-shaped emotional arc results in financially successful movies irrespective of genre and production budget. Implications of this analysis for generating on-demand content and improving productivity in entertainment industries are discussed.

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Section: Related Literaturementioning

confidence: 99%

Improving productivity in Hollywood with data science: Using emotional arcs of movies to drive product and service innovation in entertainment industries

Vecchio

Kharlamov

Parry

et al. 2020

Journal of the Operational Research Society

View full text Add to dashboard Cite

show abstract

“…Most of the research uses default classification settings. Default settings mostly result in under performance of the machine learning classification model (Ahmed et al 2019a;Ahmed et al 2020, Amrollahi et al 2020: the feature extraction, selection, model selection, hyper-parameter tuning and evaluation need to be optimized. Most research avenues do not consider multi-node application splitting or merger technique and the load balancing issue in the cluster of a heterogeneous environment.…”

Section: Critical Analysismentioning

confidence: 99%

A load balance multi-scheduling model for OpenCL kernel tasks in an integrated cluster

et al. 2020

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Nowadays, embedded systems are comprised of heterogeneous multi-core architectures, i.e., CPUs and GPUs. If the application is mapped to an appropriate processing core, then these architectures provide many performance benefits to applications. Typically, programmers map sequential applications to CPU and parallel applications to GPU. The task mapping becomes challenging because of the usage of evolving and complex CPU-and GPU-based architectures. This paper presents an approach to map the OpenCL application to heterogeneous multi-core architecture by determining the application suitability and processing capability. The classification is achieved by developing a machine learning-based device suitability classifier that predicts which processor has the highest computational compatibility to run OpenCL applications. In this paper, 20 distinct features are proposed that are extracted by using the developed LLVM-based static analyzer. In order to select the best subset of features, feature selection is performed by using both correlation analysis and the feature importance method. For the class imbalance problem, we use and compare synthetic minority over-sampling method with and without feature selection. Instead of hand-tuning the machine learning classifier, we use the tree-based pipeline optimization method to select the best classifier and its hyper-parameter. We then compare the optimized selected method with traditional algorithms, i.e., random forest, decision tree, Naïve Bayes and KNN. We apply our novel approach on extensively used OpenCL benchmarks, i.e., AMD and Polybench. The dataset contains 653 training and 277 testing applications. We test the classification results using four performance metrics, i.e., F-measure, precision, recall and R 2. The optimized and reduced feature subset model achieved a high F-measure of 0.91 and R 2 of 0.76. The proposed framework automatically distributes the workload based on the application requirement and processor compatibility.

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“…On the other hand, filmmaking itself is highly risky (Ahmed et al 2019;De Vany and Walls 1999). Before any expected revenue, a significant amount of capital investment is needed.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the movie production cycle is usually long, but market competition is fierce. Thus, it is critical to predicting the box office of a movie at an early stage to avoid potentially huge losses down the road (Ahmed et al 2019 ). In addition, such prediction can help film production companies minimize the opportunity cost of wasting limited development resources on mediocre or failed projects (Ghiassi et al 2015 ).…”

Section: Introductionmentioning

confidence: 99%

“…A few models have been developed for movie box office prediction based on word-of-mouth (WOM) data, which are normally generated after a movie’s official announcement. However, at this stage, the movie project has been largely developed, and its box office revenue prediction has a rather limited impact on the movie’s investment risk (Ahmed et al 2019 ). Thus, in this research, we ask the following question: How to predict a film’s box office revenue at its early stage without using post-release or post-production data?…”

Section: Introductionmentioning

confidence: 99%

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Early box office prediction in China’s film market based on a stacking fusion model

et al. 2020

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Artificial intelligence has been increasingly employed to improve operations for various firms and industries. In this study, we construct a box office revenue prediction system for a film at its early stage of production, which can help management overcome resource allocation challenges considering the significant investment and risk for the whole film production. In this research, we focus on China's film market, the second-largest box office in the world. Our model is based on data regarding the nature of a film itself without word-of-mouth data from social platforms. Combining extreme gradient boosting, random forest, light gradient boosting machine, k-nearest neighbor algorithm, and stacking model fusion theory, we establish a stacking model for film box office prediction. Our empirical results show that the model exhibits good prediction accuracy, with its 1-Away accuracy being 86.46%. Moreover, our results show that star influence has the strongest predictive power in this model.

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Pre-production box-office success quotient forecasting

Cited by 24 publications

References 29 publications

Improving productivity in Hollywood with data science: Using emotional arcs of movies to drive product and service innovation in entertainment industries

Improving productivity in Hollywood with data science: Using emotional arcs of movies to drive product and service innovation in entertainment industries

A load balance multi-scheduling model for OpenCL kernel tasks in an integrated cluster

Early box office prediction in China’s film market based on a stacking fusion model

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