Movienet: A Movie Multilayer Network Model using Visual and Textual Semantic Cues

TL;DR

This paper introduces a multilayer network model that integrates visual and textual semantic cues from movies to better analyze and understand movie content and narration.

Contribution

It presents a novel multilayer network framework combining script, subtitles, and visual content for comprehensive movie analysis, surpassing single-facet models.

Findings

Effective representation of movie content through the multilayer network

Successful analysis of the Star Wars saga using the proposed model

Demonstrated advantages over traditional single-facet analysis methods

Abstract

Discovering content and stories in movies is one of the most important concepts in multimedia content research studies. Network models have proven to be an efficient choice for this purpose. When an audience watches a movie, they usually compare the characters and the relationships between them. For this reason, most of the models developed so far are based on social networks analysis. They focus essentially on the characters at play. By analyzing characters' interactions, we can obtain a broad picture of the narration's content. Other works have proposed to exploit semantic elements such as scenes, dialogues, etc. However, they are always captured from a single facet. Motivated by these limitations, we introduce in this work a multilayer network model to capture the narration of a movie based on its script, its subtitles, and the movie content. After introducing the model and the extraction process from the raw data, we perform a comparative analysis of the whole 6-movie cycle of the Star Wars saga. Results demonstrate the effectiveness of the proposed framework for video content representation and analysis.