Understanding User Evaluation of Emerging AI Agents: Evidence from YouTube Comments on OpenClaw

Abstract

The rapid rise in autonomous AI agents is reshaping personal task automation and human–AI collaboration. This study develops a theoretically informed framework, drawing on the Elaboration Likelihood Model, the Technology Acceptance Model, and the sense of virtual community, to examine how comment topics and sentiment polarity relate to comment endorsement and how these associations vary across levels of reply activity. Using 55,502 raw comments from 1000 popular YouTube videos about OpenClaw, we retained 14,974 high-quality English comments for analysis. We used BERTopic to identify the main comment topics, and RoBERTa to classify sentiment polarity. The results show that (1) topics related to functionality are positively associated with comment endorsement, whereas those related to security and hardware are negatively associated with comment endorsement, and (2) the interaction associations involving reply count vary across different topics and sentiment polarity. These findings reveal users’ key concerns and emotional responses toward the emerging OpenClaw technology and offer theoretically informed and practical implications for iteratively improving AI agent tools.