Relationships between classical factors, social factors and box office collections

Biramane, Vinay; Kulkarni, Himanshu; Bhave, Anand; Kosamkar, Pranali

doi:10.1109/iota.2016.7562691

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…According to the characteristics of the film's score, competition, star, film type, technical effect, whether it is a sequel or not, and the number of screens, the neural network is used for modeling, and good results are achieved. Biramane et al [25] collected the data of movie features and social interaction from various social platforms (such as IMDb, YouTube, and Wikipedia), and established a prediction model by establishing the relationship between classic features, social media features, and movie box office success. The results showed that the prediction model established by combining classic and social factors has high accuracy.…”

Section: Related Research Workmentioning

confidence: 99%

Movie Box Office Prediction Based on Multi-Model Ensembles

Dong

Zou

et al. 2022

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This paper is based on the box office data of films released in China in the past, which was collected from ENDATA on 30 November 2021, providing 5683 pieces of movie data, and enabling the selection of the top 2000 pieces of movie data to be used as the box office prediction dataset. In this paper, some types of Chinese micro-data are used, and a Baidu search of the index data of movie names 30 days before and after the release date, coronavirus disease 2019 (COVID-19) data in China, and other characteristics are introduced, and the stacking algorithm is optimized by adopting a two-layer model architecture. The first layer base learners adopt Extreme Gradient Boosting (XGBoost), the Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), the Gradient Boosting Decision Tree (GBDT), random forest (RF), and support vector regression (SVR), and the second layer meta-learner adopts a multiple linear regression model, to establish a box office prediction model with a prediction error, Mean Absolute Percentage Error (MAPE), of 14.49%. In addition, in order to study the impact of the COVID-19 epidemic on the movie box office, based on the data of 187 movies released from January 2020 to November 2021, and combined with a number of data features introduced earlier, this paper uses LightGBM to establish a model. By checking the importance of model features, it is found that the situation of the COVID-19 epidemic at the time of movie release had a certain related impact on the movie box office.

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Section: Related Research Workmentioning

confidence: 99%