Abstract: This research study uses the Multinomial Nave Bayes (MNB) algorithm to categorize and analyze the user experience (UX) of users of mobile commerce applications. The goal of the study is to give business owners insightful information on how well their mobile applications are performing. The study's goals are to establish evaluation standards for categorizing user experiences, use MNB to classify user experience reviews to their appropriate UX elements, analyze the results of the classification, and suggest areas for improvement to enhance the usability of m-commerce. The research plan consists of a number of sprints, including data extraction, data cleaning, classification system creation using the Multinomial Naive Bayes algorithm, and model accuracy rate evaluation. The proposed system integrates the algorithm and uses data from m-commerce applications. The results of the analysis provide insights into the different UX elements such as Value, Adoptability, Desirability, and Usability. The analysis's findings shed light on many UX components like Value, Adoptability, Desirability, and Usability. The classification model was evaluated for accuracy, achieving a result of 89.243%. This means that the model correctly classified 89.243% of the user experience reviews in the evaluation dataset, indicating a satisfactory level of accuracy. However, there were some misclassifications in the remaining 10.757% of the reviews. Therefore, the research successfully developed a system that analyzed and classifies user experiences from customer reviews using MNB. The classification model demonstrated a satisfactory level of accuracy. The findings provide valuable insights and recommendations for improving the mobile application browsing experience based on user feedback and experiences.