Painter Profile Clustering Using NLP Features

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 qualitative 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. Copyright © 2024 by SCITEPRESS - Science and Technology Publications, Lda.