@Article{cmc.2025.061702,
AUTHOR = {Faheem Shaukat, Naveed Ejaz, Rashid Kamal, Tamim Alkhalifah, Sheraz Aslam, Mu Mu},
TITLE = {Multi-Label Movie Genre Classification with Attention Mechanism on Movie Plots},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {83},
YEAR = {2025},
NUMBER = {3},
PAGES = {5595--5622},
URL = {http://www.techscience.com/cmc/v83n3/60995},
ISSN = {1546-2226},
ABSTRACT = {Automated and accurate movie genre classification is crucial for content organization, recommendation systems, and audience targeting in the film industry. Although most existing approaches focus on audiovisual features such as trailers and posters, the text-based classification remains underexplored despite its accessibility and semantic richness. This paper introduces the Genre Attention Model (GAM), a deep learning architecture that integrates transformer models with a hierarchical attention mechanism to extract and leverage contextual information from movie plots for multi-label genre classification. In order to assess its effectiveness, we assess multiple transformer-based models, including Bidirectional Encoder Representations from Transformers (BERT), A Lite BERT (ALBERT), Distilled BERT (DistilBERT), Robustly Optimized BERT Pretraining Approach (RoBERTa), Efficiently Learning an Encoder that Classifies Token Replacements Accurately (ELECTRA), eXtreme Learning Network (XLNet) and Decoding-enhanced BERT with Disentangled Attention (DeBERTa). Experimental results demonstrate the superior performance of DeBERTa-based GAM, which employs a two-tier hierarchical attention mechanism: word-level attention highlights key terms, while sentence-level attention captures critical narrative segments, ensuring a refined and interpretable representation of movie plots. Evaluated on three benchmark datasets Trailers12K, Large Movie Trailer Dataset-9 (LMTD-9), and MovieLens37K. GAM achieves micro-average precision scores of 83.63%, 83.32%, and 83.34%, respectively, surpassing state-of-the-art models. Additionally, GAM is computationally efficient, requiring just 6.10 Giga Floating Point Operations Per Second (GFLOPS), making it a scalable and cost-effective solution. These results highlight the growing potential of text-based deep learning models in genre classification and GAM’s effectiveness in improving predictive accuracy while maintaining computational efficiency. With its robust performance, GAM offers a versatile and scalable framework for content recommendation, film indexing, and media analytics, providing an interpretable alternative to traditional audiovisual-based classification techniques.},
DOI = {10.32604/cmc.2025.061702}
}