Identifying research trends in computer engineering and computer science master's programs in Türkiye using topic modeling techniques: LDA, TOP2VEC, AND BERTOPIC

This thesis aims to identify research trends in Computer Engineering and Computer Science Master's programs in Turkey through the application of topic modeling techniques. In this thesis, BERTopic, Top2Vec and LDA methodologies are used to identify the research topics of 6,174 Computer Engineering and Computer Science Master's theses published in Turkey between 2020 and 2024, obtained through the YOK thesis database. According to the results, both LDA and BERTopic techniques yielded the best results in terms of coherence score, while LDA showed superior performance in terms of the perplexity metric. The findings reveal that theses in 2020 mainly focused on data analysis and software applications, while machine learning was a prominent research area in 2021. In 2022, image processing and machine learning topics are prominent, and in 2023, machine learning and algorithm theory. Finally, in 2024, the most popular topics are artificial intelligence and natural language processing. The results of this study provide university administrators with a data-driven methodology and forward-looking insights to align their academic and research agendas with the evolving national landscape of computer engineering and computer science research.