Abstract
COVID-19 a pandemic created a devastating impact throughout the world on the basis of social losses, economical loss and political loss. Opinion of individuals on the basis of feelings, thoughts, attitude and emotions should be expressed which is known as sentiments. The aim of analysis is to determine or reflect the impact of pandemic COVID-19 over the various countries. In this, we evaluate the impact of lockdown on various countries on the basis of various parameters like size of lockdown, death ratio, recovery ratio, etc. In this, reviews of individuals gathered from news reports, blogs, social media, survey forms and authenticated web pages. The sentiments are categorized into positive, negative and neutral using K-nearest neighbor algorithm of machine learning. Twitter being the more popular site for analyzing the sentiments is used. Tweepy and Node-RED tools are used to extract data from twitter, and Octoparse is used to scrap data from various news channel portals and blogs. Classification algorithm is used on the gathered data. Extraction of feature is achieved using N-gram modeling technique.
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Chauhan, D., Singh, C. (2021). Sentimental Analysis on Impact of COVID-19 Outbreak. In: Agrawal, S., Kumar Gupta, K., H. Chan, J., Agrawal, J., Gupta, M. (eds) Machine Intelligence and Smart Systems . Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-4893-6_21
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DOI: https://doi.org/10.1007/978-981-33-4893-6_21
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