Optimizing BERTopic: Analysis and Reproducibility Study of Parameter Influences on Topic Modeling

This paper reproduces key experiments and results from the BERTopic neural topic modeling framework. We validate prior findings regarding the role of text preprocessing, embedding models and term weighting strategies in optimizing BERTopic’s modular pipeline. Specifically, we show that advanced embedding models like MPNet benefit from raw input while simpler models like GloVe perform better with preprocessed text. We also demonstrate that excluding outlier documents from the topic model provides minimal gains. Additionally, we highlight that appropriate term weighting schemes, such as \(\sqrt{TF}\hbox {-}BM25(IDF)\) , are critical for topic quality. We manage to reproduce prior results and our rigorous reproductions affirm the effectiveness of BERTopic’s flexible framework while providing novel insights into tuning its components for enhanced topic modeling performance. The findings offer guidance and provide insightful refinements and clarifications, serving as a valuable reference for both researchers and practitioners applying clustering-based neural topic modeling.