DEEP LEARNING PARADIGMS IN CULTURAL ANALYTICS: BRIDGING ARTIFICIAL INTELLIGENCE AND SOCIETAL TRANSFORMATION
The fast growth of digitized museum collections has generated novel methodological requirements of cultural analytics, especially automated organization and interpretation of massive cultural heritage metadata. This paper will analyze how AI-based cultural analytics can be aided with the help of deep learning, creating a supervised metadata classification model based on the Metropolitan Museum of Art Open Access Dataset. The dataset includes a big collection of records of cultural objects with institutional, textual, material, chronological, artist-associated and cultural descriptors. A Deep Cultural Metadata Classifier was designed to predic
Resource information
- Authors
- Shivani Tanwar, Vishalkumar Parmar, Jayesh Kumar, Deepa Nehra, Dr. Pubali Mitra Paul
- Research area
- Artificial intelligence
- Research topics
- cultural analytics, deep learning, museum metadata, artificial intelligence, digital heritage, cultu
- Publication date
- 2026
- DOI
- 10.5281/zenodo.20158663
- Upstream last modified
- Sep 1, 2026
- License
- Creative Commons Attribution 4.0 International
- Last record update
- Nov 6, 2026
About this resource
The fast growth of digitized museum collections has generated novel methodological requirements of cultural analytics, especially automated organization and interpretation of massive cultural heritage metadata. This paper will analyze how AI-based cultural analytics can be aided with the help of deep learning, creating a supervised metadata classification model based on the Metropolitan Museum of Art Open Access Dataset. The dataset includes a big collection of records of cultural objects with institutional, textual, material, chronological, artist-associated and cultural descriptors. A Deep Cultural Metadata Classifier was designed to predict the category of the museum departments based on the metadata of composite objects, and the text-based feature representation, feature selection, neural classification, and comparison with a traditional sparse-text baseline were used. The findings demonstrate that museum metadata have a significant predictive organization, particularly in the object names, material descriptions, classification labels and institutional descriptors. The results also indicate that there is a class imbalance and sparsity in cultural-geographic fields that affect consistency in classification in different departments. The research is relevant to cultural analytics in that the metadata of museums is viewed as a computationally interpretable cultural knowledge structure as opposed to an administrative register. In practice, it shows how AI-based classification can be used to aid in the organization of digital heritage, museum information systems, cataloguing processes, cultural education, and more general access to open cultural collections.