What Users Think of Generative AI: A Cross-Platform NLP Analysis of Trust and Friction in App Store Reviews

用户如何看待生成式 AI：基于应用商店评论的跨平台 NLP 信任与摩擦分析

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.

摘要： 生成式 AI (GenAI) 应用已实现快速的消费者普及，但目前鲜有大规模研究探讨用户感知的质量、信任度及采用障碍。我们对六款主流 GenAI 应用（ChatGPT、Gemini、Microsoft Copilot、Claude、DeepSeek 和 Perplexity）的应用商店评论进行了首次跨应用分析之一，研究涵盖了来自 Google Play 和 Apple App Store 的 17,012 条英文评论。

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.

我们结合了 BERTopic 主题建模与 RoBERTa 情感分类，并使用卡方检验、Kruskal-Wallis 检验以及带有 Bonferroni 校正的多项逻辑回归来评估跨应用之间的差异。上述两个分析组件均通过 300 条评论的分层样本进行了人工编码验证。

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.

结果显示，负面情绪主要集中在广告（91%）、身份验证（89%）、服务器稳定性（83%）和订阅定价（73%）方面。不同应用之间的情感差异显著，其中 Claude 的负面情绪比例最高（47.7%），但同时拥有一个非常热情的用户群体，这表明存在统计学意义上的两极分化。尽管各应用的评论数量不等，但这些发现依然具有稳健性。

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.

作为探索性观察，部分 DeepSeek 的评论提出了与其中国背景相关的地缘政治和数据隐私担忧；同时，我们提出的“信任摩擦评分”（Trust Friction Score）将各应用特有的信任与可用性障碍总结为可解释的维度。本研究为消费者生成式 AI 应用中的用户信任、可用性及采用障碍提供了经过验证且具可操作性的证据。