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2024 | OriginalPaper | Buchkapitel

Offensive Language Detection in Under-Resourced Algerian Dialectal Arabic Language

verfasst von : Oussama Boucherit, Kheireddine Abainia

Erschienen in: Big Data, Machine Learning, and Applications

Verlag: Springer Nature Singapore

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Abstract

This paper addresses the problem of detecting the offensive and abusive content in Facebook comments, where we focus on the Algerian dialectal Arabic which is one of the under-resourced languages. The latter has a variety of dialects mixed with different languages (i.e., Berber, French, and English). In addition, we deal with texts written in both Arabic and Roman scripts (i.e., Arabizi). Due to the scarcity of works on the same language, we have built a new corpus regrouping more than 8.7 k texts manually annotated as normal, abusive, and offensive. We have conducted a series of experiments using the state-of-the-art classifiers of text categorization, namely: BiLSTM, CNN, FastText, SVM, and NB. The results showed acceptable performances, but the problem requires further investigation on linguistic features to increase the identification accuracy.

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Metadaten
Titel
Offensive Language Detection in Under-Resourced Algerian Dialectal Arabic Language
verfasst von
Oussama Boucherit
Kheireddine Abainia
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
Buch
Big Data, Machine Learning, and Applications

Print ISBN: 978-981-9934-80-5

Electronic ISBN: 978-981-9934-81-2

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-981-99-3481-2_49

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