Impact of COVID-19 on Human Health using Social Media Sentiment Analysis

Abstract

Several social media analysis work requires sentiment analysis. The Multi-Layer Perceptron technique is used for analyzing tweet sentiment. It is getting harder and harder to precisely discover and arrange interesting events from vast social media data, which is useful for browsing, searching, and monitoring social events for people and governments due to the Internet's explosive rise in social events. The scope of the unique (COVID-19) epidemic has resulted in severe financial hardships, stress, tension, and other future-related concerns. Networks' positive behavior can be estimated in part because of web-based media. Regarding preprocessing and feature extraction, the research needs to offer higher accuracy. Identify feelings and behaviors in comments, hashtags, posts, and tweets. The primary keyword, "COVID" or "Coronavirus," is the one that is most frequently used by combining NLP (natural language processing) and Multi-Layer Perceptron toward sentiment classification with an accuracy of 0.91 %.