[Submitted on 20 Jul 2026]
Title:What Users Think of Generative AI: A Cross-Platform NLP Analysis of Trust and Friction in App Store Reviews
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Abstract:Generative AI (GenAI) applications have achieved rapid consumer adoption, yet little large-scale research examines user-perceived quality, trust, and adoption barriers. We present one of the first cross-application analyses of app store reviews for six major GenAI applications (ChatGPT, Gemini, Microsoft Copilot, Claude, DeepSeek, and Perplexity), comprising 17,012 English-language reviews from Google Play and the Apple App Store. We combine BERTopic topic modeling with RoBERTa sentiment classification and evaluate cross-application differences using chi-square, Kruskal-Wallis, and multinomial logistic regression with Bonferroni correction. Both components are validated against human coding using a stratified sample of 300 reviews. Results show that negative sentiment concentrates in advertising (91%), authentication (89%), server reliability (83%), and subscription pricing (73%). Sentiment differs significantly across applications, with Claude exhibiting the highest negative sentiment (47.7%) alongside a strongly enthusiastic user base, indicating statistically significant polarization. These findings are robust despite unequal review counts across applications. As exploratory observations, a subset of DeepSeek reviews raised geopolitical and data privacy concerns related to its Chinese origin, while a proposed Trust Friction Score summarizes application-specific trust and usability barriers into interpretable dimensions. The study provides validated and actionable evidence on user trust, usability, and adoption barriers in consumer generative AI applications.
Comments: Submitted to Array (Elsevier); currently under peer review
Subjects:
Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Information Retrieval (cs.IR)
Cite as: arXiv:2609.19151 [cs.CL]
(or arXiv:2609.19151v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.19151
arXiv-issued DOI via DataCite
Submission history
From: Md Jafrin Hossain [view email] [v1] Mon, 20 Jul 2026 14:06:10 UTC (4,166 KB)
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