Roman Urdu toxic comment classification.
With the increasing popularity of user-generated content on social media, the number of toxic texts is also on the rise. Such texts cause adverse effects on users and society at large, therefore, the identification of toxic comments is a growing need of the day. While toxic comment classification ha...
| Publicado en: | Language Resources & Evaluation Vol. 55; no. 4; pp. 971 - 997 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
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Springer Nature
Dec2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=152947516&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 152947516 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Dec2021 vid: 55 iid: 4 pid: 237 pub: Springer Nature artinfo: ui: 152947516 10.1007/s10579-021-09530-y ppf: 971 ppct: 26 formats: fmt: – @attributes: type: T – @attributes: type: P size: 675KB tig: atl: Roman Urdu toxic comment classification. aug: au: Saeed, Hafiz Hassaan Ashraf, Muhammad Haseeb Kamiran, Faisal Karim, Asim Calders, Toon affil: Information Technology University, Lahore, Pakistan Lahore University of Management Sciences, Lahore, Pakistan University of Antwerp, Antwerp, Belgium su: User-generated content Classification Machine learning Romans Social media South Asia sug: subj: South Asia User-generated content Classification Machine learning Romans Social media keyword: Deep ensemble Deep learning Roman Urdu Roman Urdu toxic comments Toxic comment classification ab: With the increasing popularity of user-generated content on social media, the number of toxic texts is also on the rise. Such texts cause adverse effects on users and society at large, therefore, the identification of toxic comments is a growing need of the day. While toxic comment classification has been studied for resource-rich languages like English, no work has been done for Roman Urdu despite being a widely used language on social media in South Asia. This paper addresses the challenge of Roman Urdu toxic comment detection by developing a first-ever large labeled corpus of toxic and non-toxic comments. The developed corpus, called RUT (Roman Urdu Toxic), contains over 72 thousand comments collected from popular social media platforms and has been labeled manually with a strong inter-annotator agreement. With this dataset, we train several classification models to detect Roman Urdu toxic comments, including classical machine learning models with the bag-of-words representation and some recent deep models based on word embeddings. Despite the success of the latter in classifying toxic comments in English, the absence of pre-trained word embeddings for Roman Urdu prompted to generate different word embeddings using Glove, Word2Vec and FastText techniques, and compare them with task-specific word embeddings learned inside the classification task. Finally, we propose an ensemble approach, reaching our best F1-score of 86.35%, setting the first-ever benchmark for toxic comment classification in Roman Urdu. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2021. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2021 holdings: @attributes: islocal: N |
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