Analysis Text of Hate Speech Detection Using Recurrent Neural Network

Abstract

In today's social media, especially Twitter is very important for the success and destruction of one's image due to the many sentences of opinion that can compete the users. Examples of phrases that mean evil refer to hate speech to others. Evil perspectives can be categorized in hate speech, which hates speech is regulated in Article 28 of the ITE Law. Not a few people who intentionally and unintentionally oppose social media that contain hate speech. Unfortunately, social media does not have the ability to aggregate information about an existing conversation into a conclusion. One way to draw conclusions from aggregation results is to use text mining. In this paper to classify whether the text in the sentence contains elements of hate speech or not. The author hopes in this paper can make how to classify element of hate speech in the text by a computer, which later speech of hate can be recognized. By using Deep Learning method with Recurrent Neural Network (RNN) algorithm. After the creation of this program, it is hoped the computer can know and classify the existence of hate speech in the sentence. From the results of tests that have been done the average precision of 91%, recall 90% and accuracy 91%