Background Mixed reality (MR) devices provide real-time environments for physical-digital interactions across many domains. Owing to the unprecedented COVID-19 pandemic, MR technologies have supported many new use cases in the health care 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 among 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 the features of HoloLens 2 (Microsoft Corporation) that are helpful for certain fields and purposes. Methods To investigate the user sentiment, we collected 8492 tweets on a wearable MR headset, HoloLens 2, during the initial 10 months since its release in late 2019, coinciding with the onset of the pandemic. Human annotators rated the individual tweets as positive, negative, neutral, or inconclusive. Furthermore, by hiring an interannotator to ensure agreements between the annotators, we used various word vector representations to measure the impact of specific words on sentiment ratings. Following the 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%. Furthermore, the results showed an apparent change in sentiment during the COVID-19 pandemic period. During the onset of the pandemic, 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 pandemic also aligns with drastic changes in the increased number of practical insights for MR developers, researchers, and users and positive net sentiments toward the HoloLens 2 characteristics. Conclusions Our approach suggests a simple yet effective way to survey public opinion about new hardware devices quickly. The findings of this study contribute to a holistic understanding of public perception and acceptance of MR technologies during the COVID-19 pandemic and highlight several new implementations of HoloLens 2 in health care. We hope that these findings will inspire new use cases and technological features.

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.