  Harmful speech has various forms and it has been plaguing the social media in
different ways. If we need to crackdown different degrees of hate speech and
abusive behavior amongst it, the classification needs to be based on complex
ramifications which needs to be defined and hold accountable for, other than
racist, sexist or against some particular group and community. This paper
primarily describes how we created an ontological classification of harmful
speech based on degree of hateful intent, and used it to annotate twitter data
accordingly. The key contribution of this paper is the new dataset of tweets we
created based on ontological classes and degrees of harmful speech found in the
text. We also propose supervised classification system for recognizing these
respective harmful speech classes in the texts hence.
