Implementasi Metode Backpropagation Neural Network Berbasis Lexicon Based Features dan Bag Of Words untuk Identifikasi Ujaran Kebencian pada Twitter - Muhammad Mishbahul Munir; Mochamad Ali Fauzi; Rizal Setya Perdana (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
Hate speech is a language that expresses a hatred of a group or individual who intends to insult or humiliate and the media can be found anywhere, one of them Twitter. Twitter is a social media that allows users to express feelings and opinions through tweets, including tweets that contain hate speech. Document or tweet data comes from previous research on hate speech. The method used in processing the document data is Backpropagation Neural Network with feature updates using Lexicon Based Features combined with Bag of Words. In this study using data as much as 500 data is divided into training data as much as 400 data and test data as much as 100 data. From the evaluation test results, when using Lexicon Based Features, the average value of f-measure is 0%, worse than using the Bag of Words with an average f-measure of 76.638%, while when Lexicon Based Features is combined with the Bag of Words got the best average score among the previous features with a f-measure of 78.081%. And the result Backpropagation Neural Network using Lexicon Based Features combined with Bag of Words is not better than Random Forest Decision Tree using n-gram from previous research.