<![CDATA[Background: The escalating volume of user review data is necessitating automated methods for extracting valuable insights. Topic modeling was a vital method for understanding key discussions and user opinions. However, there was no comprehensive analysis of the scientific work specifically on topic modeling applied to user review datasets, including its main applications and a comparative analysis of the strengths and limitations of identified methods. This study addressed the gap by characterizing the scientific discussion, identifying potential directions, and exploring currently underutilized application areas within the context of user review analysis. Objective: This study aimed to recognize the implementation trend of topic modeling in various areas and to comprehend the methodology that could be applied to the user review dataset. Methods: A systematic literature review (SLR) was adopted by implementing Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines within six-year spans, narrowing 1746 to 28 selected primary studies. Results: The underlying insight was that user reviews had been critical as the primary data for topic modeling in analyzing various applications. Digital banking and transportation applications were the sectors that received the greatest attention. In this context, Latent Dirichlet Allocation (LDA) was the most extensively used method, with a focus on overcoming its limitations by incorporating additional strategies into LDA-based models. Conclusion: The bibliometric analysis and mapping study practically contributed as a reference when assessing the dominant topic in similar app categories and topic modeling algorithms. Furthermore, this study comprehensively analyzed various topic modeling algorithms, presenting both the strengths and weaknesses of informed selection in relevant applications. Considering the keywords cluster analysis, service quality could be adopted based on the output of the topic modeling. Keywords: Topic modeling, User review, Systematic literature review, Bibliometric analysis]]>
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