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Vulgar Remarks Detection in Chittagonian Dialect of Bangla
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Abstract
The negative effects of online bullying and harassment are increasing with Internet popularity, especially in social media. One solution is using natural language processing (NLP) and machine learning (ML) methods for the automatic detection of harmful remarks, but these methods are limited in low-resource languages like the Chittagonian dialect of Bangla. This study focuses on detecting vulgar remarks in social media using supervised ML and deep learning algorithms. Logistic Regression achieved promising accuracy (0.91) while simple RNN with Word2vec and fastTex had lower accuracy (0.84-0.90), highlighting the issue that NN algorithms require more data.
Title: Vulgar Remarks Detection in Chittagonian Dialect of Bangla
Description:
Abstract
The negative effects of online bullying and harassment are increasing with Internet popularity, especially in social media.
One solution is using natural language processing (NLP) and machine learning (ML) methods for the automatic detection of harmful remarks, but these methods are limited in low-resource languages like the Chittagonian dialect of Bangla.
This study focuses on detecting vulgar remarks in social media using supervised ML and deep learning algorithms.
Logistic Regression achieved promising accuracy (0.
91) while simple RNN with Word2vec and fastTex had lower accuracy (0.
84-0.
90), highlighting the issue that NN algorithms require more data.
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