A methodology to characterize and compute public perception via social networks

Abstract

Literature shows that the business experts and the data scientist always look at new technologies and its impact on their data centers. Nowadays, in terms of green computing, Internet of Things (IoT), Artificial Intelligence (AI), and Virtual/Augmented Reality (V/AR) are considered and adapted as new technologies. Though, in numerous studies, individual impact of each technology is investigated and reported. However, to the best of our knowledge, there exists no such study that describes the public sentiments and perception regarding V/AR, IoT, and AI; that are required to understand the public demand and improve the business process. In this paper, we propose a computation method for the public perception of new technology(y/ies) in various aspects. Topic modeling, sentiment analysis, and statistical techniques are applied to make the proposed method functional. Subsequently, we have performed a case study, that comprises on 147 million public tweets extracted from the Twitter social network. Moreover, our main contributions are related to the understanding of, (1) Distribution, (2) public perception, and (3) correlation of IoT, AI, and V/AR. The main outcome of the proposed study are; (1) More tweets on AI (51.51%) rather than V/AR (18.37%) and IoT (30.11%), (2) positive comments for IoT and negative comments are identified via sentiment analysis. (3) Some of the noteworthy terms found are blockchain, futurist, user-experience, users-demand, bonus, and presale. These all have been identified as sub-topics for each keyword describing the mutual relationship among the topics. This study is easy to replicate in terms of adaptation of new technology(y/ies) for sustainability and evolution of business process and data centers on the basis of public perception.