Automatic Text Classification on Blogs Using Support Vector Machines (SVM)

Abstract

Automatic Text Classification is a machine learning task that automatically assigns a given text document to a set of pre-defined categories based on the features extracted from its textual content. Most online communication forums, including social media, enable users to express themselves freely, and most times, anonymously. The ability to freely express oneself is a human right that should be cherished, but people always induce and spread hate or illegal words towards another group as an abuse of this liberty. For instance many online forums such as Facebook, YouTube, and Twitter consider hate speech harmful, and have policies to remove hate speech content. This paper attempts to automatically classify the textual entries made by bloggers on various topics into hate speech and non-hate speech. This was achieved by following steps like pre-processing, feature extraction and support vector machine classification. Empirical evaluation of this binary classification has resulted in an accuracy of approximately 83% over the test set. In addition to classifying the textual entries of the blogs, it is proposed that the extracted features themselves be further classified under more meaningful heads which results in generation of a semantic resource that lends greater understanding to the classification task. This semantic resource can be used for data mining requirements that arise in the future.