A Lexical Analysis of online Reviews on Human-AI Interactions
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866915623045431296 |
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| author | Arbab, Parisa Fang, Xiaowen |
| author_facet | Arbab, Parisa Fang, Xiaowen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_13480 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Lexical Analysis of online Reviews on Human-AI Interactions Arbab, Parisa Fang, Xiaowen Human-Computer Interaction Artificial Intelligence H.5.2; I.2.6 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. |
| title | A Lexical Analysis of online Reviews on Human-AI Interactions |
| topic | Human-Computer Interaction Artificial Intelligence H.5.2; I.2.6 |
| url | https://arxiv.org/abs/2511.13480 |