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UNDERSTANDING USER EXPERIENCE OF META GLASSES
IN THE ERA OF ARTIFICIAL INTELLIGENCE

Nguyen Dang Khoa* 1

1 University of Economics Ho Chi Minh City, Vietnam.
(*E-mail: khoand@ueh.edu.vn)

ABSTRACT
This study examines user experience of Meta Glasses through computational
sentiment analysis of user reviews, employing machine learning techniques and Python-
based visualization tools to transform textual feedback into actionable insights. Analyzing
user reviews collected from online platforms, the research identifies key dimensions of
user satisfaction and dissatisfaction, revealing patterns in sentiment distribution,
temporal trends, and feature-specific perceptions. The findings demonstrate that while
Meta Glasses receive predominantly positive sentiment (approximately 80% of reviews),
critical issues emerge around product condition concerns, particularly with refurbished or
pre-owned units, and battery performance challenges. Through advanced natural
language processing and visual analytics including word clouds, n-gram analysis, radar
charts, and sentiment polarity mappings, this study contributes to understanding how AI-
integrated wearable technology is perceived by consumers. The results have implications
for sustainable technology development, highlighting the importance of quality control in
refurbished products and the need for enhanced battery solutions in next-generation
smart glasses. This research demonstrates the efficacy of sentiment analysis as a tool for
product development feedback loops, offering insights for both technology
manufacturers and sustainability initiatives in the wearable tech industry.
Keywords: Meta glasses; sentiment analysis; user experience; consumer feedback
analysis.

1. Introduction
The integration of artificial intelligence into wearable technology represents a
transformative shift in human-computer interaction, with smart glasses emerging as a
pivotal interface between physical and digital realities. Meta Glasses, developed through
a collaboration between Meta Platforms and Ray-Ban, exemplifies this convergence by
combining fashion-forward design with advanced AI capabilities, including hands-free
photography, real-time audio streaming, and voice-activated assistance. Unlike earlier
attempts at smart eyewear that prioritized functionality over aesthetics, Meta Glasses
address a critical gap in the market by delivering technology that users actually want to
wear in public settings.
This research employs a comprehensive methodological framework utilizing Python-
based natural language processing libraries, including sentiment analysis algorithms, text
preprocessing techniques, and advanced visualization methods. By transforming
unstructured review text into quantifiable sentiment scores and visual representations,
this study identifies patterns in user satisfaction across multiple dimensions including
audio quality, battery performance, camera functionality, connectivity, comfort, and AI
features. The temporal analysis reveals how sentiment evolves over product lifecycle

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