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.

在线社交网络平台的指数增长导致了大量的用户生成文本内容，这包括评论和评价。为了解决这一挑战，机器学习和自然语言处理算法已经应用于分析在线可用的大量文本数据。在最近几年，主题建模技术在该领域中获得了显著的流行度。本研究全面研究和比较了特定应用于客户评价的五种常用主题建模方法，通过在实际场景中展示它们在检测重要主题方面的益处，旨在突出它们的功效。我们通过精心选择两个文本数据集来评估这些主题建模方法的性能，评估是基于标准的统计评估指标如主题连贯性得分。我们的发现揭示了 BERTopic 在提取有意义的主题方面始终表现出色并取得了良好的结果。