Smart Sentimental Analysis of the Impact of Social Media on COVID-19

Coronavirus or COVID-19 pandemic has spread to 210 countries and territories taking the lives of more than 140,000 people globally as per the record on April 2020. This worldwide outburst is a predominant topic of discussion on the social media. This paper investigates the Twitter data to analyze the sentiment of the public regarding this pandemic. Long Short-Term Memory (LSTM) algorithm-based Recurrent Neural Network (RNN) architecture is implemented for performing sentiment analysis. Further, data related to coronavirus are collected from social media sources such as Blogs, Forum, News, Videos, Web, Podcast using hashtags related to coronavirus. Data from 15 March 2020 to 23 March 2020 are used for analysis. From this analysis, it is found that the information related to corona virus has considerably influenced the social media. People are well aware of this outbreak. Analysis has also shown that there is less spread of false information regarding coronavirus unlike SARS, MERS, etc. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.