A Semantically Enhanced Deep Neural Network Framework for Reputation System in Web Mining for Covid-19 Twitter Dataset

Abstract

With the web containing a huge amount of information, the extraction of application oriented understandable data has become easier with web mining. Web Mining is the area that is derived from data mining. Unlike data mining, web mining is used to extract interesting patterns from the information available on the web. When used with deep learning, the pattern recognition becomes much easier. Deep learning works in the same way, how a human brain works in terms of predicting the outcomes when a bulk of information is provided. It deals with mathematical models that recognize the patterns efficiently. The different types of web mining techniques, namely: web content mining (WCM), web structure mining (WSM) and web usage mining (WUM) persists. Researchers and economists around the globe are keen in knowing the impact of the pandemic on the society’s economic status; this work helps find the same using reputation system. As twitter is a hub of different opinions of public, we work with covid- 19 data set from twitter. A reputation system helps finding the socio economic status of the tweets regarding covid-19 dataset. This paper has proposed a framework in which the web mining is implemented using a semantic enhanced deep neural network technique for the reputation system.