Sentiment Analysis of Covid-19 Tweets using Twitter Database–A Global Scenario

Sentiment analysis falls within the category of Natural Language Processing (NLP) technology. Behaviour analysis and recommendation employ sentiment analysis. On the dataset of COVID-19 tweets, random forest Classifier, Decision Tree, Support Vector Machine (SVM), and Random Forest are the machine learning methods under consideration for sentiment analysis. The total number of tweets used for this research is 179108. Pre-Processing is used to clean and analyze these tweets. The sentiment analysis of COVID-19 tweets used in this study reveals the individual experiences of those affected by the epidemic. The primary goal of this survey is to analyze people’s experiences. It helps to better understand the emotions of people, especially during an epidemic period. Twitter, a microblogging platform, contains a sizable collection of datasets that illustrate a wide range of human emotions, including fear, happy, sad, anger, and joy etc. Sentiment analysis is crucial for gauging the consensus of the populace. This research can help us assess the potential impact of a pandemic on the general public’s decision-making.