Asia Pacific Journal of Information Systems (APJIS)

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Date	September 2026
Vol. No.	Vol. 36 No. 3
DOI	https://doi.org/10.14329/apjis.2026.36.3.725
Page	725~756
Title	Classifying UX-Related Reviews in Vertical Commerce Apps Using the 7C Framework
Author	Juhyun Lee, Joongsan Oh
Keyword	Vertical Commerce Mobile Apps, UX-related Reviews, 7C Framework, Topic Modeling, KcBERT
Abstract	This study examines the potential of the 7C framework as a common organizing structure for review-based UX monitoring in mobile vertical commerce apps. We develop and evaluate the model-development phase of a staged pipeline that identifies UX-related reviews, extracts topics, maps them to 7C categories, and trains a Korean pretrained language model (KcBERT) to reproduce the resulting label structure at the individual-review level. Among 51,505 Google Play reviews from ten apps, manual labeling of 5,100 reviews followed by Naïve Bayes classification yielded 8,260 UX-related reviews (16.0%). LDA produced 11 interpretable topics, including login errors, server speed, delivery/customer support, and product search/filtering. These topics were mapped to 7C categories to generate rule-based training labels. Against these labels, KcBERT achieved 66.16% accuracy and a macro F1-score of 62.81%, outperforming a TF-IDF logistic regression baseline. In a separate human validation of 420 UX-related reviews, the rule-based labels, KcBERT, and TF-IDF achieved accuracy/macro F1 values of 0.6762/0.6683, 0.6333/0.6240, and 0.5405/0.5068, respectively. These findings provide proof-of-concept evidence that the 7C framework can serve as a basis for organizing large-scale app-review data, while application of the trained classifier in the proposed operational monitoring phase remains to be empirically evaluated.

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