Enhancing Natural Language Processing with LDA-BERT Integration: A Comprehensive Review of Methodologies, Applications, and Future Directions

Natural Language Processing (NLP) plays a critical role in deciphering unstructured text data. This review paper explores the integration of Latent Dirichlet Allocation (LDA) and Bidirectional Encoder Representations from Transformers (BERT) in various NLP tasks. We examine how this hybrid model enhances traditional NLP methodologies, particularly addressing the limitations of the kMeans clustering algorithm and expanding to diverse applications like sentiment analysis and topic discovery. Through an analysis of recent studies, the paper highlights the versatility and effectiveness of LDA-BERT in handling complex data structures, mitigating outlier influences, and adapting to varied data densities. The lack of standardized benchmarks for evaluating these models and their diverse application contexts are discussed as key challenges. The review emphasizes the necessity for future research to develop more uniform evaluation frameworks to better assess the effectiveness of LDA-BERT models across different NLP tasks.