Abstract
From 2019, the world is facing an unforeseen challenge in the form of COVID-19, which started in Wuhan (China), and within two months, it spread to 212 countries. The coronavirus disease (COVID-19) pandemic puts unprecedented pressure on healthcare systems worldwide.
Due to its rapid widespread around the globe affecting the lives of millions, extensive measures to reduce and prevent its transmission have been implemented. One of which is to shut down their cities completely.
During this Pandemic, people started to express their situations through social media tools. In natural language processing, valuable insights can be captured from textual data taken from different social media platforms.
In this research work, data related to COVID-19 is collected from a popular social networking site, Twitter. The tweets gathered are refined through pre-processing for text mining and sentiment analysis.
From this data, we successfully detect the actual count of people who may be affected by the COVID-19 Pandemic using sentimental analysis and machine learning techniques.