Sentiment Analysis of Consumer Post Covid Utilizing Quick Mining Approaches

Abstract: Consumers and families are challenged by the Post-COVID-19 to maintain a healthy lifestyle, as unhealthful behaviors raise the mortality risk. In this investigation, we look at how the prevalent Corona virus has affected a wide range of consumer attitudes, convictions, and behavior. A variety of client data has been gathered using sentiment analysis. Additionally, utilizing a variety of quick mining methods, current breakthroughs in machine learning algorithms have enhanced sentiment analysis estimates on lifestyles. While machine learning automates the creation of logical models, a perspective's semantic orientation determines whether it is positive, negative, or neutral. This research focuses on the sentiment analysis of lifestyles utilizing quick mining approaches, classifying their polarity as good, negative, or neutral. To estimate attitudes, machine learning employs methods such as Support Vector Regression and K-means clustering.

Keywords: Machine Learning, Big Data, Sentiment Analysis, Support vector Regression (SVR), K-means