Data Analytics

Social Media Users’ Perceptions of a Wearable Mixed Reality Headset During the COVID-19 Pandemic: Aspect-Based Sentiment Analysis. JMIR Serious Games. DOI: 10.2196/36850.

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. 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.

Classification of motor vehicle crash injury severity: A hybrid approach for imbalanced data. Accident Analysis & Prevention. DOI: 10.1016/j.aap.2018.08.025

This study aims to classify the injury severity in motor-vehicle crashes with both high accuracy and sensitivity rates. The dataset used in this study contains 297,113 vehicle crashes, obtained from the Michigan Traffic Crash Facts (MTCF) dataset, from 2016–2017. Similar to any other crash dataset, different accident severity classes are not equally represented in MTCF. To account for the imbalanced classes, several techniques have been used, including under-sampling and over-sampling. Using five classification learning models (i.e., Logistic regression, Decision tree, Neural network, Gradient boosting model, and Naïve Bayes classifier), we classify the levels of injury severity and attempt to improve the classification performance by two training-testing methods including Bootstrap aggregation (or bagging) and majority voting. Furthermore, due to the imbalance present in the dataset, we use the geometric mean (G-mean) to evaluate the classification performance. We show that the classification performance is the highest when bagging is used with decision trees, with over-sampling treatment for imbalanced data. The effect of treatments for the imbalanced data is maximized when under-sampling is combined with bagging. In addition to the original five classes of injury severity in the MTCF dataset, we consider two additional classification problems, one with two classes and the other with three classes, to (1) investigate the impact of the number of classes on the performance of classification models, and (2) enable comparing our results with the literature.

Analysis of Trust in Automation Survey Instruments Using Semantic Network Analysis. In International Conference on Applied Human Factors and Ergonomics. DOI: 10.1007/978-3-319-94334-3_2.

This study analyzed existing survey instruments to provide an integrated list of keywords/constructs to measure the various perceptions of trust building in automation. While the trust between users and automated functions or systems has been an area of substantial research interest to understand the interactions between human and automation, research efforts to measure the trust to date have led to inconclusive and mixed outcomes. Of the existing scales for measuring trust in automation, inadequate development of constructs and the lack of reliability and validity have been identified as major causes for such outcomes. To develop a scale in a more objective and systematic approach, 86 keywords from existing 9 survey instruments were identified. The keyword network was developed based on the semantic textural similarity, and the network centrality analysis provided total 14 keywords with high centraility and degree matrics. The results can suggest some potential solutions about the lack of consistency and the wide array of constructs without adequate analytic justification in prior survey instruments. The outcomes will be utilized to develop a new integrated scale that can be generally applicable to a wide variety of automation adoption or, with slight modifications, in most trust in automation applications.