The proliferation of the internet has provided many tools and platforms for consumers to share and create content concerning their experiences with different products and services. Customer reviews are considered essential both in strategy development and consumer attraction. By analyzing customer reviews, companies can decide on product enhancement and development, service recovery and improvements, pricing, and customer recruitment and retention. Although different methodologies have been used to understand the impact of customer reviews in the last decade, a new realm of studies began to benefit from text analytics and machine learning algorithms. Within these methodologies, topic modeling algorithms emerge as the most popular analytical tool. However, despite the subsistence of different topic modeling algorithms, their exposure in marketing research has been limited. This study aims to provide information on different algorithms’ semantic potential in analyzing customer reviews. Using a comprehensive dataset extracted from the Best Buy platform, the contributions of various algorithms are compared based on data preprocessing, algorithm implementation, and their semantic ability in marketing research. Based on different stages of implementation and their respective complexity, the results indicate efficacy in using BERTopic in analyzing customer reviews.