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Authors: N. Ilba ; U. Yıldırım and Doruk Sen

Affiliation: Department of Industrial Engineering, Istanbul Bilgi University, Eyupsultan, Istanbul, Turkey

Keyword(s): Natural Language Processing, Clustering, Text Analysis, XAI.

Abstract: This study introduces a practice for clustering painter profiles using features obtained from natural language processing (NLP) techniques. The investigation of similarities among painters plays an essential function in art history. While most existing research generally focuses on the visual comparison of the artists’ work, more studies should examine the textual content available for artists. As the volume of online textual information grows, the frequency of discussions about artists and their creations has gained importance, underscoring the connection between social visibility through digital discourse and an artist’s recognition. This research provides a method for investigating Wikipedia profiles of painters using NLP attributes. Among unsupervised machine learning algorithms, the K-means is adopted to group the painters using the driven attributes from the content details of their profile pages. The clustering results are evaluated through a benchmark painter list and a quali tative review. The model findings reveal that the suggested approach effectively clusters the presented benchmark painter profiles, highlighting the potential of textual data analysis on painter profile similarities. (More)

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Paper citation in several formats:
Ilba, N., Yıldırım, U. and Sen, D. (2024). Painter Profile Clustering Using NLP Features. In Proceedings of the 9th International Conference on Complexity, Future Information Systems and Risk - COMPLEXIS; ISBN 978-989-758-698-9; ISSN 2184-5034, SciTePress, pages 91-98. DOI: 10.5220/0012623700003708

@conference{complexis24,
author={N. Ilba and U. Yıldırım and Doruk Sen},
title={Painter Profile Clustering Using NLP Features},
booktitle={Proceedings of the 9th International Conference on Complexity, Future Information Systems and Risk - COMPLEXIS},
year={2024},
pages={91-98},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012623700003708},
isbn={978-989-758-698-9},
issn={2184-5034},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Complexity, Future Information Systems and Risk - COMPLEXIS
TI - Painter Profile Clustering Using NLP Features
SN - 978-989-758-698-9
IS - 2184-5034
AU - Ilba, N.
AU - Yıldırım, U.
AU - Sen, D.
PY - 2024
SP - 91
EP - 98
DO - 10.5220/0012623700003708
PB - SciTePress

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