Drug Usage Analysis by VADER Sentiment Analysis on Leading Countries


  • Sandipan Biswas Brainware University, Kolkata.
  • Shivnath Ghosh Brainware University, Kolkata.


Clustering, Opinion, Sentiments, Tweets, VADER


In Twitter people from all part of the planet will build their opinions and take feed from the opinions which produces around five hundred million of tweets daily that amounts regarding 8TB of information. Data which are scrapped from Twitter may be helpful if analyzed as we are able to extract vital information via sentiment analysis. Opinions or comments regarding any news or launch of a product of quite trend may be ascertained well in twitter information. Our aim is to analysis of tweets on use of drugs for the treatment of COVID-19 diseases. In twitter sentiment analysis, we have a tendency to categorize those tweets into positive and negative sentiment and cluster along or a pack of cluster. We have conducted a study and locate that the application will quickly and efficiently distinguish numerous tweets on the idea of their sentiment scores and proportion and may notice weak and powerful positive or negative tweets once clustered with results of various dictionaries and establish a powerful support on our assumption. This paper surveys the polarity activity exploitation using VADER sentiment analysis on the utilization of drug for of COVID-19 treatment.

Author Biographies

Sandipan Biswas, Brainware University, Kolkata.

Department of Computer Science and Engineering, Brainware University, Kolkata, West Bengal, India.

Shivnath Ghosh, Brainware University, Kolkata.

Department of Computer Science and Engineering, Brainware University, Kolkata, West Bengal, India.


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