  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.
