Text Mining and Topic Modeling for Sport Science Literature // A comprehensive toolkit for mining, analyzing, and visualizing scientific literature in sport science domains. Provides functions for retrieving abstracts from 'Scopus', preprocessing text data, performing advanced topic modeling using Latent Dirichlet Allocation ('LDA'), Structural Topic Models ('STM'), and Correlated Topic Models ('CTM'), and creating publication- ready visualizations including keyword co-occurrence networks and topic trends. For methodological details see Blei et al. (2003) doi:10.1162/jmlr.2003.3.4-5.993 for 'LDA', Roberts et al. (2014) doi:10.1111/ajps.12103 for 'STM', and Blei and Lafferty (2007) doi:10.1214/07-AOAS114 for 'CTM'.