Open AccessCOVID19 Sentiment Analysis using Machine Learning Classification Algorithms
Author(s) -
Kusumanchi Naga Sireesha and Padala Srinivasa Reddy
Publication year - 2021
Publication title -
international journal of modern trends in science and technology
Language(s) - English
Resource type - Journals
ISSN - 2455-3778
DOI - 10.46501/ijmtst0709003
Subject(s) - sentiment analysis , computer science , social media , python (programming language) , social media analytics , machine learning , artificial intelligence , covid-19 , context (archaeology) , analytics , microblogging , data science , natural language processing , world wide web , medicine , geography , disease , archaeology , pathology , infectious disease (medical specialty) , operating system
Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fuelledby incomplete and often inaccurate information. There is therefore a tremendous need to address and better understandCOVID-19’s informational crisis. The diverse use of social networking sites, like Twitter, speeds up the process of sharinginformation and having views on community events and health crises COVID-19 has been one of Twitter's trending areas. TheTwitter messages created via Twitter are named Tweets.In this paper, we identify public sentiment associated with the pandemic using Coronavirus-specific Tweets and Python,along with its sentiment analysis packages. We provide an overview of two essential machine learning classification methods, inthe context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. Thisresearch provides insights into Coronavirus fear sentiment progression, associated methods, limitations, and differentopportunities. In this project, we have designed a Sentiment analysis System that would identify the sentiment of a tweet andclassify it into one of the five classes they include:”ExtremelyPositive”,“Positive”,”Neutral”, ”Negative” and “ExtremelyNegative”.