COVID-19 TWEETS SENTIMENT ANALYSIS USING MACHINE LEARNING MODELS
Social media platforms are tremendously being used for sharing news, ideas, opinions, and comments about various happenings in the world. Twitter is one such platform where health-related information and news are shared by both the official sources and the citizens. Significant changes in the lifestyle of adults as well as children due to the COVID-19 imposed lockdown have also been a topic of discussion in the public forum. A huge amount of misinformation is also being spread. Understanding people's sentiments and outlook towards the coronavirus will help health agencies take actions like encouraging and providing positive and factual information. This study focuses on identifying and performing sentiment analysis which will help to explore a enormous amounts of tweets related to COVID-19. It classifies and distinguishes clearly between the different emotions of tweets like positive tweets, negative tweets, and neutral tweets. It also supports predicting the sentiment for unknown tweets. The exploratory data analysis performed on the tweets helps to identify the count of tweets according to location, time, and sentiment. In a lockdown, social media being the only source of understanding the public sentiments about the coronavirus, it is important to take into account the publics opinions and reactions and information being circulated in all major forms.