13 International Journal for Modern Trends in Science and Technology As per UGC guidelines an electronic bar code is provided to seure your paper International Journal for Modern Trends in Science and Technology, 7(09): 13-18, 2021 Copyright © 2021 International Journal for Modern Trends in Science and Technology ISSN: 2455-3778 online DOI: https://doi.org/10.46501/IJMTST0709003 Available online at: http://www.ijmtst.com/vol7issue09.html COVID19 Sentiment Analysis using Machine Learning Classification Algorithms Kusumanchi Naga Sireesha 1 , Padala Srinivasa Reddy 2 1 PG Scholar, Department of Computer Science, SVKP & Dr K S Raju Arts & Science College, Penugonda, W.G.Dt., A.P, India. 2 Associate Professor in Computer Science, SVKP & Dr K S Raju Arts & Science College, Penugonda, W.G.Dt., A.P, India. To Cite this Article Kusumanchi Naga Sireesha and Padala Srinivasa Reddy. COVID19 Sentiment Analysis using Machine Learning Classification Algorithms. International Journal for Modern Trends in Science and Technology 2021, 7, 0709007, pp. 13-18. https://doi.org/10.46501/IJMTST0709003 Article Info Received: 09 August 2021; Accepted: 31 August 2021; Published: 01 September 2021 Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fuelled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19’s informational crisis. The diverse use of social networking sites, like Twitter, speeds up the process of sharing information and having views on community events and health crises COVID-19 has been one of Twitter's trending areas. The Twitter 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, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. This research provides insights into Coronavirus fear sentiment progression, associated methods, limitations, and different opportunities. In this project, we have designed a Sentiment analysis System that would identify the sentiment of a tweet and classify it into one of the five classes they include:”ExtremelyPositive”,“Positive”,”Neutral”, ”Negative” and “Extremely Negative”. KEYWORDS: covid, healthcare, nlp, machine learning, text data, tweeter, social media, sentiment analysis, text vectorization 1.INTRODUCTION Nowadays, the Internet is becoming worldwide popular, and it is serving as a cost-effective platform for information carriers by the rapid enlargement of social media. Several social media platforms like blogs, reviews, posts, tweets are being processed for extracting the people’s opinions about a particular product, organization, or situation. The attitude and feelings comprise an essential part in evaluating the behaviour of an individual that is known as sentiments. These sentiments can further be analyzed towards an entity, known as sentiment analysis or opinion mining. By using sentiment analysis, we can interpret the sentiments or emotions of others and classify them into different categories that help an organization to know people’s emotions and act accordingly. This analysis depends on its expected outcomes, e.g., analyzing the text depending on its polarity and emotions, feedback about a particular feature, and analyzing the text in different languages require detection of the respective language. ABSTRACT