A Lexicon-based Approach for Hate Speech Detection

We explore the idea of creating a classifier that can be used to detect presence of hate speech in web discourses such as web forums and blogs. In this work, hate speech problem is abstracted into three main thematic areas of race, nationality and religion. The goal of our research is to create a model classifier that uses sentiment analysis techniques and in particular subjectivity detection to not only detect that a given sentence is subjective but also to identify and rate the polarity of sentiment expressions. We begin by whittling down the document size by removing objective sentences. Then, using subjectivity and semantic features related to hate speech, we create a lexicon that is employed to build a classifier for hate speech detection. Experiments with a hate corpus show significant practical application for a real-world web discourse.