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.