Exploring film therapy in digital health: text mining study of Google search data

Abstract

Against the backdrop of the rapid development of digital health technology and the increasing prominence of mental health problems, film therapy as an innovative intervention method is gaining more and more attention. This study systematically collected information related to film therapy on the Google search platform and used multidimensional text mining technology to conduct an in-depth analysis of data sources, aiming to reveal the presentation characteristics and development trends of film therapy in cyberspace. Eight core topic categories were identified using the latent Dirichlet allocation (LDA) model, covering a complete spectrum from innovative treatment methods to diversified application scenarios, among which character emotional resonance and professional guidance became the two most prominent themes. Sentiment calculation analysis found that positive attitudes dominated the online discussions, with up to three-quarters of the content expressing positive evaluations and no negative emotional content appearing, reflecting the public’s widespread recognition of this treatment method. The results of the semantic association network construction showed that key concepts such as “therapy,” “movie,” “use,” and “people” formed a close semantic cluster, reflecting the inherent logic of the film therapy theory system and the systematic nature of the practice framework. Vocabulary frequency statistics further confirmed the high integration characteristics of treatment terms and film and television terms, indicating that this interdisciplinary field has formed a relatively mature discourse system. The research findings provide important empirical support and theoretical guidance for the standardized development of film therapy, the promotion of clinical applications, and the construction of a digital mental health service system.