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BACKGROUND Mixed reality (MR) devices provide real-time environments for physical-digital interaction across many domains. Due to the unprecedented COVID-19 pandemic, mixed reality technologies supported many new use cases in the healthcare industry, enabling social distancing practices to minimize the risk of contact and transmission. Despite their novelty and increasing popularity, public evaluations are sparse and often rely on social interactions between users, developers, researchers, and potential buyers. OBJECTIVE The purpose of this study is to use aspect-based sentiment analysis to explore changes in sentiment during the onset of the COVID-19 pandemic as new use cases emerged in the health care industry, to characterize net insights for MR developers, researchers, and users, and to analyze features of HoloLens 2 that are helpful for certain fields and purposes. METHODS To investigate the user sentiment, we collected 8,492 tweets on a wearable mixed reality headset, Microsoft HoloLens 2, during the initial ten months since its release in late 2019, coinciding with the onset of the pandemic. Then human annotators rated individual tweets as positive, negative, neutral, and inconclusive classes. Further, by hiring an inter-annotator to ensure agreements between the annotators, we used various word vector representations to measure the impact of specific words on the sentiment ratings. Following sentiment classification for each tweet, we trained a model for sentiment analysis via supervised learning. RESULTS The results of our sentiment analysis showed that the Bag of Words tokenizing method using a Random Forest supervised learning approach produced the highest accuracy of the test set at 81.29%. Further, the results show an apparent change in sentiment through the pandemic. During the pandemic onset, consumer goods were severely affected, which aligns with a drop in both positive and negative sentiment. Following this, there is a sudden spike in positive sentiment, hypothesized to be caused by the new use cases of the device in health care education and training. This time also aligns with drastic changes in the increased number of practical insights for MR developers, researchers, and users and positive net sentiments towards the HoloLens 2 characteristics. CONCLUSIONS Our approach suggests a simple yet effective idea to survey public opinion about new hardware devices quickly. The study's findings contribute to a holistic understanding of public perception and acceptance of MR technologies during the COVID-19 pandemic and highlight several new implementations of the HoloLens 2 in health care. We hope such findings inspire new use cases and technology features.

This abstract is a companion piece to the ninth iteration of the “Me and My VE” interactive session. The purpose of this alternative format session is to allow human factors researchers to demonstrate how they use virtual reality, mixed reality, and virtual environments to support end-user research. The session begins with each presenter providing a brief introduction of their virtual reality, mixed reality, or virtual environment application. After this introduction, attendees engage with the presenters and their virtual environments in an interactive demonstration period. This year’s demonstrations include applications ranging from designing virtual environments for accessibility to using virtual and extended reality to support medical and aviation training, content validation, and human-computer interaction studies. The goal of the session is to allow attendees to experience how human factors professionals use virtual environments to support human factors-oriented research and to learn about the exciting work being conducted in this field.