Open AccessDetecting Hate Speech In Twitter Using Long Short-Term Memory and Naïve Bayes Method
Author(s) -
Firman Sriyono,
Kusrini Kusrini,
Asro Nasiri
Publication year - 2022
Publication title -
syntax literate
Language(s) - English
Resource type - Journals
eISSN - 2548-1398
pISSN - 2541-0849
DOI - 10.36418/syntax-literate.v7i2.6313
Subject(s) - indonesian , naive bayes classifier , term (time) , social media , computer science , bayes' theorem , long short term memory , process (computing) , psychology , internet privacy , artificial intelligence , linguistics , world wide web , support vector machine , bayesian probability , philosophy , physics , quantum mechanics , recurrent neural network , artificial neural network , operating system
The information technologi’s development has been very sophisticated and easy, so that it becomes a lifestyle for people throughout the world without exception Indonesia which also affected by the development of this technology. One of the benefits of information technology is the emergence various kinds of social networking sites or social media such as Facebook, Twitter and Instagram. Technological developments isn’t only have a positive impact, but also have a negative impact the crime of insult or hate speech. This study is aims to classify Indonesian hate speech sentences based on hate speech and neutral sentiments using the Long Short-Term Memory (LSTM) method. Research data is obtained from Indonesian-language tweets. In testing process, the LSTM method will be compared with the Naïve Bayes method