Monitoring Student Well-being: Using AI to Detect Offensive Language in Educational Platforms

Abstract

Because of the quickly developing communication modes of individuals in informal communities, individuals bought into these interpersonal organisations at an extraordinary rate to convey and impart their contemplations to different supporters. Twitter was chosen for this study because of its notoriety and simple admission to information. This review proposes a strategy to recognise oppressive substance in Twitter information that contains tweets, retweets, and remarks. Oppressive language is arranged in view of elements through the component extraction interaction and classes, either harmful or not. The motivation behind this significant advance is to provide the advantages and disadvantages of each approach, which will be useful for dealing with significant stages. While growing new systems or strategies to distinguish harmful substances in the client content of informal organisations. Furthermore, this correlation provides additional information on which system is suitable for determining the degree of disagreeableness to further develop exactness.