TOPIC DISTRIBUTION AND TREND ANALYSIS OF THESES IN HEALTHCARE MANAGEMENT: AN EXAMINATION USING THE BERTopic APPROACH
Sağlık Yönetiminde Tezlerin Konu Dağılımı ve Trend Analizi
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Keywords:
Health Management, BERTopic, Natural Language Processing, Topic ModelingAbstract
The primary aim of this study is to examine thesis data in the field of healthcare management through BERTopic, an advanced algorithm combining data mining and natural language processing techniques. A total of 6,514 theses obtained from the Council of Higher Education Thesis Center database were processed using natural language processing methods such as text cleaning, tokenization, stopword removal, lemmatization, and TF-IDF weighting. Subsequently, the Transformer-based BERTopic model was applied to analyze the topic distribution and thematic structures of the theses. The analysis revealed that master’s theses were predominant, with significant variations across universities, and identified 10 main topics of varying sizes. Moreover, the trend analysis showed a steady increase in thesis production until 2019, followed by a declining pattern in the last five years. This study highlights the diversity of academic production in healthcare management in terms of type and topic, and the findings provide valuable insights for shaping both academic policies and research strategies.
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