ACADSTAFF UGM

CREATION
Title : IDENTIFYING HATE SPEECH IN BAHASA INDONESIA WITH LEXICON-BASED FEATURES AND SYNONYM-BASED QUERY EXPANSION
Author :

Atmaja Wikandiputra (1) Afiahayati, S.Kom., M.Cs., Ph.D (2) Vincent Michael Sutanto (3)

Date : 1 2022
Keyword : Hate speech, Classication, Support Vector Machine, Lexicon-based, Synonym Hate speech, Classication, Support Vector Machine, Lexicon-based, Synonym
Abstract : Freedom of social media users who are not controlled in giving opinions can make it easier for users to attack certain people, objects, or environments with hateful language or commonly called hate speech. According to the Indonesia Criminal Investigation Police, 80% of cybercrimes reported were expressions of hatred. Preventive actions taken by Facebook & Twitter are deemed ineffective because checking hate speech is still manually through user reports. In this study, we used a machine learning algorithm, which is Support Vector Machine (SVM), to identify whether a speech is considered as hate speech or not. We combined the SVM with the Lexicon-based Features and Synonym-based Query Expansion method. The models were trained and evaluated by calculating Accuracy, Precision, Recall, and F-measure. This study shows that the use of the Synonym-based Query Expansion method can improve the performance of the SVM model with Lexicon-based as its feature.
Group of Knowledge : Ilmu Komputer
Original Language : English
Level : Internasional
Status :
Published
Document
No Title Document Type Action
1 PaperAjus.pdf
Document Type : [PAK] Full Dokumen
[PAK] Full Dokumen View
2 Form-L1_PaperAtmaja.pdf
Document Type : Dokumen Pendukung Karya Ilmiah (Hibah, Publikasi, Penelitian, Pengabdian)
Dokumen Pendukung Karya Ilmiah (Hibah, Publikasi, Penelitian, Pengabdian) View