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.

互联网的普及为消费者提供了许多工具和平台来分享和创建有关他们对不同产品和服务的体验的内容。客户评论对于战略制定和消费者吸引都被认为至关重要。通过分析客户评论，公司可以决定产品增强和开发、服务恢复和改进、定价以及客户招募和保留。尽管在过去十年中使用了不同的方法来理解客户评论的影响，但一个新的研究领域开始受益于文本分析和机器学习算法。在这些方法中，主题建模算法成为最流行的分析工具。然而，尽管存在不同的主题建模算法，他们在营销研究方面的接触有限。本研究旨在提供有关不同算法在分析客户评论时的语义潜力的信息。使用从百思买平台提取的综合数据集，根据数据预处理、算法实现及其在营销研究中的语义能力来比较各种算法的贡献。根据实施的不同阶段及其各自的复杂性，结果表明使用 BERTopic 分析客户评论的有效性。算法实现及其在营销研究中的语义能力。根据实施的不同阶段及其各自的复杂性，结果表明使用 BERTopic 分析客户评论的有效性。算法实现及其在营销研究中的语义能力。根据实施的不同阶段及其各自的复杂性，结果表明使用 BERTopic 分析客户评论的有效性。