CULTURAL ANALYTICS IN VISUAL ARTS: USING DATA SCIENCE TO UNDERSTAND AESTHETIC TRENDS

Pushpa Nagini Sripada; Keerthika K; Aswitha V; Mahesh Kumar; Anandhi D; E. Rajesh

doi:10.29121/shodhkosh.v7.i3s.2026.7325

Authors

Pushpa Nagini Sripada Professor, Department of English, Meenakshi College of Arts and Science, Meenakshi Academy of Higher Education and Research, India
Keerthika K Department of Computer Science, Meenakshi College of Arts and Science, Meenakshi Academy of Higher Education and Research, India
Aswitha V Assistant Professor, Department of English, Meenakshi College of Arts and Science, Meenakshi Academy of Higher Education and Research, India
Mahesh Kumar PG, Meenakshi College of Physiotherapy, Meenakshi Academy of Higher Education and Research, India
Anandhi D Assistant Professor / Research Scientist, Department of Biochemistry, Meenakshi Ammal Dental College and Hospital, Meenakshi Academy of Higher Education and Research, India
E. Rajesh Professor, Oral and Maxillofacial Pathology and Oral Microbiology, Sree Balaji Dental College and Hospital, Bharath Institute of Higher Education and Research (BIHER), Chennai, Tamil Nadu, India

DOI:

https://doi.org/10.29121/shodhkosh.v7.i3s.2026.7325

Keywords:

Cultural Analytics, Visual Arts Analysis, Digital Humanities, Computer Vision in Art, Aesthetic Trend Analysis, Machine Learning For Cultural Data, Computational Art History, Cultural Data Visualization

Abstract [English]

The presence of digital technologies and extensive digitization of collections of cultural heritage has established new possibilities in the use of data science approaches to the study of visual arts. Computational analysis Cultural analytics is a field of digital humanities that integrates computational analysis with large-scale art collections to explore aesthetic patterns. The study analyzes the applications of machine learning, computer vision and statistical modeling methods to detect stylistic trends and evolution of artwork in the visual culture. Organized computational system which involves textual data intake, pre-processing of images, elements of feature detection and clustering algorithms alongside visualization techniques to examine aesthetic qualities in computerized images is introduced. Analytical evidence of representative datasets of digital art data show that quantifiable trends exist in the diversity of color, the complexity of texture, the variability of composition and the subject matter representation across artistic movements. Findings indicate that there are obvious stylistic groups that can be traced back to historical artistic movements like Renaissance, Baroque, Impressionism and Modernism. Temporal analysis also suggests the growing variation in styles and colors of artistic processes in modern times as compared to prior eras. These computational findings can be interpreted with the help of visualization techniques such as trend graphs, heatmaps and cluster representations. The research shows cultural analytics is a useful interdisciplinary tool to address aesthetic development in visual arts. Data analysis is an addition to the conventional art historical interpretation because it allows exploring visual culture on a large scale. The results indicate that artificial intelligence and cultural data analytics have a promising future in the development of research in digital humanities, museum studies, and computational art history.

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