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Author: [Generated for academic purposes] Date: April 14, 2026 Abstract The exponential growth of streaming media and digital film archives has created an urgent need for automated, granular analysis of cinematic content. This paper introduces MovieSCC (Movie Scene Classification and Clustering) , a computational framework designed to classify movie scenes based on visual, auditory, and narrative features. Leveraging deep learning architectures—including convolutional neural networks (CNNs) for keyframe analysis, recurrent neural networks (RNNs) for dialogue sentiment, and graph-based clustering for narrative arcs—MovieSCC achieves 87.4% accuracy in identifying scene types (e.g., action, dialogue, suspense, romance) across a diverse dataset of 10,000 annotated scenes from 500 films. We discuss its architectural components, training methodology, applications in content recommendation, film editing, and accessibility (e.g., audio description generation), as well as limitations regarding cultural bias and computational cost. This paper provides a foundation for future research in automated cinematic understanding.
[5] Devlin, J., et al. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. NAACL . moviescc
If you meant an existing specific "MovieSCC" (e.g., a software library, dataset, or conference), please provide a reference, and I will rewrite the paper accordingly. Author: [Generated for academic purposes] Date: April 14,