Toxic comment classifier on social media platform

Abstract

The objective of the paper is to mitigate internet negativity by identifying and blocking toxic comments related to a particular topic or product. The detrimental effects of social media abuse and harassment can cause people to refrain from expressing themselves. Although some platforms disable user comments altogether, this method is not efficient. The presence of toxicity in comments can assist platforms in taking appropriate measures. The paper aims to classify comments according to their toxicity levels for future blockage. The dataset comprises comments classified into six types, toxic, severe toxic, threat, obscene, identity hate, and insult. Multiple classification techniques will be employed to determine the most accurate one for the data. The authors will employ four types of classification and select the most precise one for each dataset. This methodology enables the authors to choose various datasets for the problem and select the most accurate classifier for each dataset.