Event classification from the Urdu language text on social media

The real-time availability of the Internet has engaged millions of users around the world. The usage of regional languages is being preferred for effective and ease of communication that is causing multilingual data on social networks and news channels. People share ideas, opinions, and events that...

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Autores principales:	Malik Daler Ali Awan, Nadeem Iqbal Kajla, Amnah Firdous, Mujtaba Husnain, Malik Muhammad Saad Missen
Formato:	article
Lenguaje:	EN
Publicado:	PeerJ Inc. 2021
Materias:	Social media Event classification Resource poor Machine learning Text Natural language processing Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/74cdda872e97477aab1220104afa6ea8
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id	oai:doaj.org-article:74cdda872e97477aab1220104afa6ea8
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spelling	oai:doaj.org-article:74cdda872e97477aab1220104afa6ea82021-11-20T15:05:12ZEvent classification from the Urdu language text on social media10.7717/peerj-cs.7752376-5992https://doaj.org/article/74cdda872e97477aab1220104afa6ea82021-11-01T00:00:00Zhttps://peerj.com/articles/cs-775.pdfhttps://peerj.com/articles/cs-775/https://doaj.org/toc/2376-5992The real-time availability of the Internet has engaged millions of users around the world. The usage of regional languages is being preferred for effective and ease of communication that is causing multilingual data on social networks and news channels. People share ideas, opinions, and events that are happening globally i.e., sports, inflation, protest, explosion, and sexual assault, etc. in regional (local) languages on social media. Extraction and classification of events from multilingual data have become bottlenecks because of resource lacking. In this research paper, we presented the event classification task for the Urdu language text existing on social media and the news channels by using machine learning classifiers. The dataset contains more than 0.1 million (102,962) labeled instances of twelve (12) different types of events. The title, its length, and the last four words of a sentence are used as features to classify the events. The Term Frequency-Inverse Document Frequency (tf-idf) showed the best results as a feature vector to evaluate the performance of the six popular machine learning classifiers. Random Forest (RF) and K-Nearest Neighbor (KNN) are among the classifiers that out-performed among other classifiers by achieving 98.00% and 99.00% accuracy, respectively. The novelty lies in the fact that the features aforementioned are not applied, up to the best of our knowledge, in the event extraction of the text written in the Urdu language.Malik Daler Ali AwanNadeem Iqbal KajlaAmnah FirdousMujtaba HusnainMalik Muhammad Saad MissenPeerJ Inc.articleSocial mediaEvent classificationResource poorMachine learningTextNatural language processingElectronic computers. Computer scienceQA75.5-76.95ENPeerJ Computer Science, Vol 7, p e775 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Social media Event classification Resource poor Machine learning Text Natural language processing Electronic computers. Computer science QA75.5-76.95
spellingShingle	Social media Event classification Resource poor Machine learning Text Natural language processing Electronic computers. Computer science QA75.5-76.95 Malik Daler Ali Awan Nadeem Iqbal Kajla Amnah Firdous Mujtaba Husnain Malik Muhammad Saad Missen Event classification from the Urdu language text on social media
description	The real-time availability of the Internet has engaged millions of users around the world. The usage of regional languages is being preferred for effective and ease of communication that is causing multilingual data on social networks and news channels. People share ideas, opinions, and events that are happening globally i.e., sports, inflation, protest, explosion, and sexual assault, etc. in regional (local) languages on social media. Extraction and classification of events from multilingual data have become bottlenecks because of resource lacking. In this research paper, we presented the event classification task for the Urdu language text existing on social media and the news channels by using machine learning classifiers. The dataset contains more than 0.1 million (102,962) labeled instances of twelve (12) different types of events. The title, its length, and the last four words of a sentence are used as features to classify the events. The Term Frequency-Inverse Document Frequency (tf-idf) showed the best results as a feature vector to evaluate the performance of the six popular machine learning classifiers. Random Forest (RF) and K-Nearest Neighbor (KNN) are among the classifiers that out-performed among other classifiers by achieving 98.00% and 99.00% accuracy, respectively. The novelty lies in the fact that the features aforementioned are not applied, up to the best of our knowledge, in the event extraction of the text written in the Urdu language.
format	article
author	Malik Daler Ali Awan Nadeem Iqbal Kajla Amnah Firdous Mujtaba Husnain Malik Muhammad Saad Missen
author_facet	Malik Daler Ali Awan Nadeem Iqbal Kajla Amnah Firdous Mujtaba Husnain Malik Muhammad Saad Missen
author_sort	Malik Daler Ali Awan
title	Event classification from the Urdu language text on social media
title_short	Event classification from the Urdu language text on social media
title_full	Event classification from the Urdu language text on social media
title_fullStr	Event classification from the Urdu language text on social media
title_full_unstemmed	Event classification from the Urdu language text on social media
title_sort	event classification from the urdu language text on social media
publisher	PeerJ Inc.
publishDate	2021
url	https://doaj.org/article/74cdda872e97477aab1220104afa6ea8
work_keys_str_mv	AT malikdaleraliawan eventclassificationfromtheurdulanguagetextonsocialmedia AT nadeemiqbalkajla eventclassificationfromtheurdulanguagetextonsocialmedia AT amnahfirdous eventclassificationfromtheurdulanguagetextonsocialmedia AT mujtabahusnain eventclassificationfromtheurdulanguagetextonsocialmedia AT malikmuhammadsaadmissen eventclassificationfromtheurdulanguagetextonsocialmedia
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Event classification from the Urdu language text on social media

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