Bangla hate speech detection on social media using attention-based recurrent neural network

Hate speech has spread more rapidly through the daily use of technology and, most notably, by sharing your opinions or feelings on social media in a negative aspect. Although numerous works have been carried out in detecting hate speeches in English, German, and other languages, very few works have...

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Autores principales:	Das Amit Kumar, Al Asif Abdullah, Paul Anik, Hossain Md. Nur
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	rnn attention mechanism lstm gru bangla text classification bangla hate speech detection Science Q Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/58b89077a75f4e4788a7dad893e7f789
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id	oai:doaj.org-article:58b89077a75f4e4788a7dad893e7f789
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spelling	oai:doaj.org-article:58b89077a75f4e4788a7dad893e7f7892021-12-05T14:10:51ZBangla hate speech detection on social media using attention-based recurrent neural network2191-026X10.1515/jisys-2020-0060https://doaj.org/article/58b89077a75f4e4788a7dad893e7f7892021-04-01T00:00:00Zhttps://doi.org/10.1515/jisys-2020-0060https://doaj.org/toc/2191-026XHate speech has spread more rapidly through the daily use of technology and, most notably, by sharing your opinions or feelings on social media in a negative aspect. Although numerous works have been carried out in detecting hate speeches in English, German, and other languages, very few works have been carried out in the context of the Bengali language. In contrast, millions of people communicate on social media in Bengali. The few existing works that have been carried out need improvements in both accuracy and interpretability. This article proposed encoder–decoder-based machine learning model, a popular tool in NLP, to classify user’s Bengali comments from Facebook pages. A dataset of 7,425 Bengali comments, consisting of seven distinct categories of hate speeches, was used to train and evaluate our model. For extracting and encoding local features from the comments, 1D convolutional layers were used. Finally, the attention mechanism, LSTM, and GRU-based decoders have been used for predicting hate speech categories. Among the three encoder–decoder algorithms, attention-based decoder obtained the best accuracy (77%).Das Amit KumarAl Asif AbdullahPaul AnikHossain Md. NurDe Gruyterarticlernnattention mechanismlstmgrubangla text classificationbangla hate speech detectionScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 578-591 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	rnn attention mechanism lstm gru bangla text classification bangla hate speech detection Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	rnn attention mechanism lstm gru bangla text classification bangla hate speech detection Science Q Electronic computers. Computer science QA75.5-76.95 Das Amit Kumar Al Asif Abdullah Paul Anik Hossain Md. Nur Bangla hate speech detection on social media using attention-based recurrent neural network
description	Hate speech has spread more rapidly through the daily use of technology and, most notably, by sharing your opinions or feelings on social media in a negative aspect. Although numerous works have been carried out in detecting hate speeches in English, German, and other languages, very few works have been carried out in the context of the Bengali language. In contrast, millions of people communicate on social media in Bengali. The few existing works that have been carried out need improvements in both accuracy and interpretability. This article proposed encoder–decoder-based machine learning model, a popular tool in NLP, to classify user’s Bengali comments from Facebook pages. A dataset of 7,425 Bengali comments, consisting of seven distinct categories of hate speeches, was used to train and evaluate our model. For extracting and encoding local features from the comments, 1D convolutional layers were used. Finally, the attention mechanism, LSTM, and GRU-based decoders have been used for predicting hate speech categories. Among the three encoder–decoder algorithms, attention-based decoder obtained the best accuracy (77%).
format	article
author	Das Amit Kumar Al Asif Abdullah Paul Anik Hossain Md. Nur
author_facet	Das Amit Kumar Al Asif Abdullah Paul Anik Hossain Md. Nur
author_sort	Das Amit Kumar
title	Bangla hate speech detection on social media using attention-based recurrent neural network
title_short	Bangla hate speech detection on social media using attention-based recurrent neural network
title_full	Bangla hate speech detection on social media using attention-based recurrent neural network
title_fullStr	Bangla hate speech detection on social media using attention-based recurrent neural network
title_full_unstemmed	Bangla hate speech detection on social media using attention-based recurrent neural network
title_sort	bangla hate speech detection on social media using attention-based recurrent neural network
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/58b89077a75f4e4788a7dad893e7f789
work_keys_str_mv	AT dasamitkumar banglahatespeechdetectiononsocialmediausingattentionbasedrecurrentneuralnetwork AT alasifabdullah banglahatespeechdetectiononsocialmediausingattentionbasedrecurrentneuralnetwork AT paulanik banglahatespeechdetectiononsocialmediausingattentionbasedrecurrentneuralnetwork AT hossainmdnur banglahatespeechdetectiononsocialmediausingattentionbasedrecurrentneuralnetwork
_version_	1718371667816742912

Bangla hate speech detection on social media using attention-based recurrent neural network

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