Unsupervised Topic Modeling Using Contrastive Learning

Purpose: This study aims to propose an unsupervised topic modeling algorithm utilizing contrastive learning to overcome the limitations of existing methods.Methods: The proposed algorithm incorporates contrastive learning into topic modeling and employs an end-to-end learning process. This approach embeds documents, topics, and words into a unified space, identifying their relationships and providing more intuitive topic modeling results. The algorithm was applied to scientific and technical literature data and its performance was evaluated against existing topic modeling methods.Results: The proposed algorithm demonstrated comparable or superior quantitative metrics in terms of consistency and diversity when compared to existing topic modeling approaches on scientific and technical literature datasets. These results indicate that the embeddings learned through contrastive learning effectively capture the relationships between documents and topics.Conclusion: The unsupervised topic modeling algorithm leveraging contrastive learning provides more intuitive results compared to traditional topic modeling approaches and proves to be a valuable tool for large-scale text data analysis. Future research can explore its application in various domains and evaluate the algorithm using additional quantitative and qualitative metrics to further expand its applicability.