Identify Future Trending Topics by Thematic Mapping of the Cinema Phenomenon Using Machine Learning and LDA

This study aims to evaluate 16,891 academic publications in the field of cinema between 1980 and 2024 using bibliometric analysis and topic modeling methods. Based on data obtained from the Web of Science (WOS) and Scopus databases, bibliometric findings were received, including the distribution of publications by year, the annual number and rate of citations per article, the most productive authors in the field, the production status of authors over time, the countries of authors and the number of articles they published, and the journals with the highest number of publications. Data obtained from the Web of Science (WOS) and Scopus databases were also used to identify prominent word groups and themes in the articles using text mining and Latent Dirichlet Allocation (LDA) topic modeling. As a result of the analysis, 12 main themes emerged based on word-text relationships and the weight of publications. The findings show that cinema studies have developed with increasing momentum over the years and that there has been a growing focus on certain topics. This study systematically examines the development of cinema studies literature through descriptive content analysis and LDA topic modeling. In this respect, it is important in that it systematically reveals the structural and thematic transformation of academic production in the field of cinema and provides a theoretical and methodological basis for future research. It also makes a current and multidimensional contribution to the discipline in terms of revealing the increasingly important digital trends, cultural representations, and interdisciplinary developments in cinema studies.