Open Access

ARTICLE

Accurate Machine Learning Predictions of Sci-Fi Film Performance

Amjed Al Fahoum^1,*, Tahani A. Ghobon²

1 Biomedical Systems and Informatics Engineering Department, Hijjawi Faculty for engineering Technology, Yarmouk University, Irbid, 21163, Jordan
2 School of Engineering Technology, Al Hussein Technical University, Amman, Jordan

* Corresponding Author: Amjed Al Fahoum. Email:

Journal of New Media 2023, 5(1), 1-22. https://doi.org/10.32604/jnm.2023.037583

Received 09 November 2022; Accepted 15 May 2023; Issue published 14 June 2023

Abstract

A groundbreaking method is introduced to leverage machine learning algorithms to revolutionize the prediction of success rates for science fiction films. In the captivating world of the film industry, extensive research and accurate forecasting are vital to anticipating a movie’s triumph prior to its debut. Our study aims to harness the power of available data to estimate a film’s early success rate. With the vast resources offered by the internet, we can access a plethora of movie-related information, including actors, directors, critic reviews, user reviews, ratings, writers, budgets, genres, Facebook likes, YouTube views for movie trailers, and Twitter followers. The first few weeks of a film’s release are crucial in determining its fate, and online reviews and film evaluations profoundly impact its opening-week earnings. Hence, our research employs advanced supervised machine learning techniques to predict a film’s triumph. The Internet Movie Database (IMDb) is a comprehensive data repository for nearly all movies. A robust predictive classification approach is developed by employing various machine learning algorithms, such as fine, medium, coarse, cosine, cubic, and weighted KNN. To determine the best model, the performance of each feature was evaluated based on composite metrics. Moreover, the significant influences of social media platforms were recognized including Twitter, Instagram, and Facebook on shaping individuals’ opinions. A hybrid success rating prediction model is obtained by integrating the proposed prediction models with sentiment analysis from available platforms. The findings of this study demonstrate that the chosen algorithms offer more precise estimations, faster execution times, and higher accuracy rates when compared to previous research. By integrating the features of existing prediction models and social media sentiment analysis models, our proposed approach provides a remarkably accurate prediction of a movie’s success. This breakthrough can help movie producers and marketers anticipate a film’s triumph before its release, allowing them to tailor their promotional activities accordingly. Furthermore, the adopted research lays the foundation for developing even more accurate prediction models, considering the ever-increasing significance of social media platforms in shaping individuals’ opinions. In conclusion, this study showcases the immense potential of machine learning algorithms in predicting the success rate of science fiction films, opening new avenues for the film industry.

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Keywords

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