Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/171916
Title: Prediction of box office revenue of movies
Authors: Er, Erica Ming Chee
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2023
Publisher: Nanyang Technological University
Source: Er, E. M. C. (2023). Prediction of box office revenue of movies. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/171916
Project: SCSE22-0769 
Abstract: In the ever-evolving field of prediction of box office revenue of movies, the integration of state-of-the-art neural networks, especially BERT with traditional FNN offers promising avenues for research. This paper investigates the effectiveness of BERT-based models combined with FNNs in predicting movie box office revenues. Leveraging a comprehensive dataset comprising 36,108 entries from TMDB and enriched with metrics from IMDb, the study presents a two-pronged approach: analyzing pre-release and all-available data to simulate varying real-world scenarios. Three distinct models were proposed and assessed: a pre-trained BERT embedding model, a fine-tuned BERT variant, and an integrated hybrid model encompassing both textual and numerical data. Comparative evaluations based on loss curves, predicted vs. actual values, and overall performance metrics unveiled the superior efficacy of the integrated hybrid model, particularly when fed with comprehensive data from the all-available dataset. The results underscore the importance of a cohesive architecture that effectively processes both textual and numerical data, emphasizing the value of comprehensive data and thoughtful model selection in maximizing predictive accuracy in the realm of box office revenue forecasting.
URI: https://hdl.handle.net/10356/171916
Schools: School of Computer Science and Engineering 
Organisations: Multimedia and Interacting Computing Lab (MICL) 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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