Exploring the Ethical Concerns in User Reviews of Mental Health Apps using Topic Modeling and Sentiment Analysis

TL;DR

This paper uses NLP techniques like topic modeling and sentiment analysis on user reviews to identify ethical concerns in mental health apps, revealing gaps in existing ethical frameworks and highlighting emerging issues.

Contribution

It introduces a comprehensive NLP-based framework combining topic modeling, ethical mapping, and zero-shot classification to evaluate ethics in mental health app reviews.

Findings

Existing ethical principles are insufficient for modern AI health apps

Emerging ethical challenges are identified in user reviews

The framework can improve app fairness and trustworthiness

Abstract

The rapid growth of AI-driven mental health mobile apps has raised concerns about their ethical considerations and user trust. This study proposed a natural language processing (NLP)-based framework to evaluate ethical aspects from user-generated reviews from the Google Play Store and Apple App Store. After gathering and cleaning the data, topic modeling was applied to identify latent themes in the context of ethics using topic words and then map them to well-recognized existing ethical principles described in different ethical frameworks; in addition to that, a bottom-up approach is applied to find any new and emergent ethics from the reviews using a transformer-based zero-shot classification model. Sentiment analysis was then used to capture how users feel about each ethical aspect. The obtained results reveal that well-known ethical considerations are not enough for the modern AI-based technologies and are missing emerging ethical challenges, showing how these apps either uphold or overlook key moral values. This work contributes to developing an ongoing evaluation system that can enhance the fairness, transparency, and trustworthiness of AI-powered mental health chatbots.