An Artificial Intelligence Framework to Predict Box Office Performance Through Multimodal Analysis of Movie Trailers and Online Audience Responses

In the film industry, box office predictions are crucial for making investment decisions, marketing plans, and distribution choices. Current prediction approaches mainly use movie metadata or single-modal information, ignoring trailer video content and audience responses, resulting in low prediction accuracy and robustness. The proposed framework is novel in that it uses a Convolutional Neural Network (CNN)–Bidirectional Long Short-Term Memory (BiLSTM)–Attention (CNN–BiLSTM–Attention) regression and Artificial Rabbits Optimization (ARO)-based feature selection to integrate trailer visual features, trailer audio features, movie metadata, and audience responses into a single multimodal architecture. The proposed framework used the Movies YouTube Trailers & Sentiment dataset, which was preprocessed using mean value imputation, followed by Min–Max normalization and one-hot encoding. Vision Transformer (ViT) was used to extract visual characteristics from the trailer, and Mel-Frequency Cepstral Coefficients (MFCC), chroma, spectral centroid, zero-crossing rate, and Root Mean Square (RMS) energy were used to extract audio features. Audience response features were derived from sentiment scores, favorability, and vote counts. Visual, audio, metadata, and audience response features were fused into a unified multimodal representation. In order to forecast box office revenue, ARO chose the CNN–BiLSTM–Attention network's most informative feature subset. Mean Absolute Error (MAE) of 0.0246, Mean Squared Error (MSE) of 0.0009, Root Mean Squared Error (RMSE) of 0.0304, Mean Absolute Percentage Error (MAPE) of 0.0586, and Coefficient of Determination (R 2 ) of 0.9549 were all attained by the suggested model. The results showed that the proposed model improved prediction accuracy and provided an effective decisionsupport tool for movie producers and distributors.