An Approach to Investigate Public Opinion, Views, and Perspectives Towards Exoskeleton Technology

Abstract

Over the last decade, exoskeletons have had an extensive impact on different disciplines and application domains such as assisted living, military, healthcare, firefighting, and industries, on account of their diverse and dynamic functionalities to augment human abilities, stamina, potential, and performance in a multitude of ways. In view of this wide-scale applicability and use-cases of exoskeletons, it is crucial to investigate and analyze the public opinion, views, and perspectives towards exoskeletons which would help to interpret the effectiveness of the underlining human-robot, human-machine, and human-technology interactions. The Internet of Everything era of today's living, characterized by people spending more time on the internet than ever before, holds the potential for the investigation of the same by mining and analyzing relevant web behavior, specifically from social media, that can be interpreted to understand public opinion, views, and perspectives towards a topic or set of topics. Therefore, this paper aims to address this research challenge related to exoskeletons by utilizing the potential of web behavior-based Big Data mining in the modern-day Internet of Everything era. As Twitter is one of the most popular social media platforms on a global scale – characterized by both the number of users and the amount of time spent by its users on the platform – this work focused on investigating web behavior on Twitter to interpret the public opinion, views, and perspectives towards exoskeleton technology. A total of approximately 20,000 tweets related to exoskeletons were used to evaluate the effectiveness of the proposed approach. The results presented and discussed uphold the efficacy of the proposed approach to interpret and analyze the public opinion, views, and perspectives towards exoskeletons from the associated tweets.

Keywords: Exoskeleton, Sentiment Analysis, Web Behavior, Internet Of Everything, Big Data

DOI: 10.54941/ahfe100860