Deep Learning-Based Analysis of Emotional and Content Relevance Between Bullet Screens and Subtitles as Movie Narrative Medium

This study borrows elements of film text in the field of film studies to develop the potential of data analysis using AI algorithms, while aiming to explore the emotional and content relevances between the two narrative mediums in film, bullet screens and subtitles, as well as the narrative effect variations that they bring about. The film sample Forever Young was analyzed by deep learning, data analysis, and knowledge discovery algorithms and tools. The BERT model was used to quantify the intensity of emotions and conduct fine-grained emotional classification of film characters. Emotion curves are plotted based on time series data. Thematic words are extracted by the LDA algorithm, and text similarity is computed using Word2Vec. The study found differences in emotional expression between bullet screens and subtitles, and they also exhibit strong emotional and content relevances. Yet, this correlation is not only influenced by temporal deviation factors but also by specific emotional types, different stages of story development, and the collective emotions of online views. The conclusion suggests that the effective collection of plot metronomes in film narratives helps reveal crucial plot nodes and emotional trends in films that cause obvious emotional changes, and placing them appropriately is fundamental for continuously attracting and retaining viewers’ attention. The interaction between bullet comments and subtitles is not only about emotional expression but also enhances the effectiveness of the film narrative. Overall, this study demonstrates the importance of database logic and technology in understanding and expressing film emotions and meanings, and providing new ideas and methods for enhancing film narrative effectiveness.