Exploring User Experience by User Review Using LDA-Topic Modeling and HEART Framework: A Systematic Literature Review - Indriadika, Ayu; Santoso, Noviyanti (MALCOM: Indonesian Journal of Machine Learning and Computer Science, 2025)
This study aims to evaluate the integration of the HEART framework (Happiness, Engagement, Adoption, Retention, and Task Success) with computational modeling techniques such as Latent Dirichlet Allocation (LDA) for measuring User Experience (UX). A Systematic Literature Review (SLR) was conducted on articles published between 2015 and 2025, selected from reputable databases including Scopus. The selected studies emphasize the use of HEART metrics in conjunction with machine learning approaches, particularly LDA, and were assessed based on the Scimago journal quartile ranking system. The findings categorize the studies into five main research objectives: predicting user satisfaction and emotional response, optimizing usability, analyzing user-generated content, evaluating learning performance through gamified systems, and assessing system requirements in relation to UX. This classification reveals growing trends in applying hybrid methods that combine qualitative metrics with automated modeling techniques. The results underline the importance of developing more adaptive and scalable UX evaluation frameworks that align human-centered insights with machine learning-driven analysis. This study offers a foundational reference for future research in building integrative models that advance the depth and scale of UX assessments in complex digital environments.