Paper Title
Identification of Hatred Speeches on Twitter

Social network is a popular media for the freedom of speech. These speeches are related to the people having different religions, geographical locations and races, etc. However, different religious and ethnic groups often take exceptions for freedom of speech, which may sometimes stir people into action. Although, concerning authorities are regulating freedom of speech for social sites, automated systems are required to implement such regulations. We consider spiritual belief as most targeted domain for spreading hatred speech. This work is focused on devising a methodology that can filter tweets/messages over Twitter and identify hatred speeches. We have investigated the utility of supervised learning algorithms (i.e. Support Vector Machine (SVM), Naive Bayes (NB) and k Nearest Neighbors (kNN) for performing this task. SVM, NB and kNN classifiers are applied on Tweets for categorizing opinions first and then find their sentiment polarity. In both cases, SVM showed better performance than NB and kNN. Index Terms- Sentimental Analysis; Twitter; Freedom of Speech; Religion; Hatred Speech.