Hate speech identification in text written in Indonesian with recurrent neural network

Erryan Sazany, Indra Budi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Some researches had succeeded in doing hate speech identification automatically from text with machine learning and deep learning approaches. However, it was still unclear how adaptive is a deep learning-based model if it is tested on a different set of text data with different domain. To address this issue, this research proposed some deep learning-based methods, using some variants of Recurrent Neural Network to identify hate speech in texts sourced from Twitter, and then used to predict other set of text data sourced from Facebook and Twitter. The experiment was done in order to measure the difference of model performance between training phase and testing phase. Experiment results showed that the proposed method outperformed the machine learning based methods, both in training phase, by GRU algorithm with 85.37% F1-score, and in testing phase, by LSTM algorithm with 76.30% F1-score. Then, in terms of adaptability of model performance, the proposed method gave comparable result against the baseline method.

Original languageEnglish
Title of host publication2019 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages211-216
Number of pages6
ISBN (Electronic)9781728152929
DOIs
Publication statusPublished - Oct 2019
Event11th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2019 - Bali, Indonesia
Duration: 12 Oct 201913 Oct 2019

Publication series

Name2019 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2019

Conference

Conference11th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2019
CountryIndonesia
CityBali
Period12/10/1913/10/19

Keywords

  • Adaptability
  • Deep learning
  • Hate speech
  • Recurrent neural network
  • Text classification

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