Exploring the Power of Topic Modeling Techniques in Analyzing Customer Reviews: A Comparative Analysis

The exponential growth of online social network platforms and applications has led to a staggering volume of user-generated textual content, including comments and reviews. Consequently, users often face difficulties in extracting valuable insights or relevant information from such content. To address this challenge, machine learning and natural language processing algorithms have been deployed to analyze the vast amount of textual data available online. In recent years, topic modeling techniques have gained significant popularity in this domain. In this study, we comprehensively examine and compare five frequently used topic modeling methods specifically applied to customer reviews. The methods under investigation are latent semantic analysis (LSA), latent Dirichlet allocation (LDA), non-negative matrix factorization (NMF), pachinko allocation model (PAM), Top2Vec, and BERTopic. By practically demonstrating their benefits in detecting important topics, we aim to highlight their efficacy in real-world scenarios. To evaluate the performance of these topic modeling methods, we carefully select two textual datasets. The evaluation is based on standard statistical evaluation metrics such as topic coherence score. Our findings reveal that BERTopic consistently yield more meaningful extracted topics and achieve favorable results.