Disciplinary landscapes of deep learning: Cross-domain insights via LDA topic modeling

This study employs Latent Dirichlet Allocation (LDA) to analyze research trends in deep learning across engineering, natural sciences, and social sciences from 2020 to 2024. Using a corpus of 3,000 research paper titles, latent thematic structures were extracted to identify the major research directions within each field. The analysis uncovered four prominent topics per domain, revealing clear disciplinary differences in thematic emphasis and levels of methodological maturity. Engineering research predominantly addressed automation technologies, intelligent control systems, and real-time optimization. In contrast, natural science studies focused heavily on medical imaging, computational modeling, and data-driven scientific discovery. Social science research demonstrated an increasing integration of deep learning with ensemble modeling, prediction frameworks, and algorithmic decision processes. By offering a comparative view across disciplines, this study highlights both shared and divergent trajectories in deep learning research. The findings also suggest several future research directions, including the advancement of explainable AI techniques, the incorporation of multimodal data sources, and the development of domain-specific methodological adaptations to improve applicability and interpretability.