ANALYSIS OF SOCIO-ECONOMIC IMPACT OF COVID-19 THROUGH PUBLIC OPINION MINING USING THE SEMANTIC WEIGHING DEEP NEURAL NETWORK ARCHITECTURE AND ALGORITHM

Abstract: Web mining has made it easier to extract application-oriented intelligible data from the vast amount of data available on the internet. There is a new field called web mining that was born out of data mining. Web mining, as opposed to traditional data mining, aims to find patterns in data that has been made available to the public. Deep learning makes it much easier to recognize patterns. Deep learning operates in the same manner as the human brain does when it comes to anticipating outcomes from a large amount of data. A key focus is on developing mathematical models that can detect patterns fast. WCM, WSM, and WUM (web usage mining) are only a few of the web-mining approaches that have been developed in the past several years. To better understand how the epidemic affects society's economic standing, this initiative uses a reputation system to gather information from researchers and economists around the world. We deal with the covid- 19 data collection from twitter because it is a hub of varied public viewpoints. The socio-economic status of tweets about the Covid-19 dataset can be determined using a reputation system. For the reputation system, this research proposes a framework in which web mining is accomplished using a semantic augmented deep neural network technique