A Lexical Analysis of online Reviews on Human-AI Interactions

TL;DR

This paper analyzes nearly 56,000 online reviews to uncover key factors influencing human-AI interactions, aiming to inform the development of more user-centric AI systems.

Contribution

It introduces a lexical analysis approach to identify specific user concerns and challenges in human-AI interactions from large-scale online reviews.

Findings

Key factors influencing human-AI interaction identified

Initial factor analysis reveals significant concerns and challenges

Content analysis provides deeper insights into user perceptions

Abstract

This study focuses on understanding the complex dynamics between humans and AI systems by analyzing user reviews. While previous research has explored various aspects of human-AI interaction, such as user perceptions and ethical considerations, there remains a gap in understanding the specific concerns and challenges users face. By using a lexical approach to analyze 55,968 online reviews from G2.com, Producthunt.com, and Trustpilot.com, this preliminary research aims to analyze human-AI interaction. Initial results from factor analysis reveal key factors influencing these interactions. The study aims to provide deeper insights into these factors through content analysis, contributing to the development of more user-centric AI systems. The findings are expected to enhance our understanding of human-AI interaction and inform future AI technology and user experience improvements.