Threatening URDU Language Detection from Tweets Using Machine Learning
Technology’s expansion has contributed to the rise in popularity of social media platforms. Twitter is one of the leading social media platforms that people use to share their opinions. Such opinions, sometimes, may contain threatening text, deliberately or non-deliberately, which can be disturbing...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/20/10342 |
| _version_ | 1797475620563714048 |
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| author | Aneela Mehmood Muhammad Shoaib Farooq Ansar Naseem Furqan Rustam Mónica Gracia Villar Carmen Lili Rodríguez Imran Ashraf |
| author_facet | Aneela Mehmood Muhammad Shoaib Farooq Ansar Naseem Furqan Rustam Mónica Gracia Villar Carmen Lili Rodríguez Imran Ashraf |
| author_sort | Aneela Mehmood |
| collection | DOAJ |
| description | Technology’s expansion has contributed to the rise in popularity of social media platforms. Twitter is one of the leading social media platforms that people use to share their opinions. Such opinions, sometimes, may contain threatening text, deliberately or non-deliberately, which can be disturbing for other users. Consequently, the detection of threatening content on social media is an important task. Contrary to high-resource languages like English, Dutch, and others that have several such approaches, the low-resource Urdu language does not have such a luxury. Therefore, this study presents an intelligent threatening language detection for the Urdu language. A stacking model is proposed that uses an extra tree (ET) classifier and Bayes theorem-based Bernoulli Naive Bayes (BNB) as the based learners while logistic regression (LR) is employed as the meta learner. A performance analysis is carried out by deploying a support vector classifier, ET, LR, BNB, fully connected network, convolutional neural network, long short-term memory, and gated recurrent unit. Experimental results indicate that the stacked model performs better than both machine learning and deep learning models. With 74.01% accuracy, 70.84% precision, 75.65% recall, and 73.99% F1 score, the model outperforms the existing benchmark study. |
| first_indexed | 2024-03-09T20:47:44Z |
| format | Article |
| id | doaj.art-7ea93ad47a154bbb83d93a92500a2500 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T20:47:44Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-7ea93ad47a154bbb83d93a92500a25002023-11-23T22:43:06ZengMDPI AGApplied Sciences2076-34172022-10-0112201034210.3390/app122010342Threatening URDU Language Detection from Tweets Using Machine LearningAneela Mehmood0Muhammad Shoaib Farooq1Ansar Naseem2Furqan Rustam3Mónica Gracia Villar4Carmen Lili Rodríguez5Imran Ashraf6Department of Computer Science, University of Management and Technology, Lahore 54000, PakistanDepartment of Computer Science, University of Management and Technology, Lahore 54000, PakistanDepartment of Computer Science, University of Management and Technology, Lahore 54000, PakistanSchool of Computer Science, University College Dublin, D04 V1W8 Dublin, IrelandFaculty of Social Science and Humanities, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, SpainFaculty of Social Science and Humanities, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, SpainDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, KoreaTechnology’s expansion has contributed to the rise in popularity of social media platforms. Twitter is one of the leading social media platforms that people use to share their opinions. Such opinions, sometimes, may contain threatening text, deliberately or non-deliberately, which can be disturbing for other users. Consequently, the detection of threatening content on social media is an important task. Contrary to high-resource languages like English, Dutch, and others that have several such approaches, the low-resource Urdu language does not have such a luxury. Therefore, this study presents an intelligent threatening language detection for the Urdu language. A stacking model is proposed that uses an extra tree (ET) classifier and Bayes theorem-based Bernoulli Naive Bayes (BNB) as the based learners while logistic regression (LR) is employed as the meta learner. A performance analysis is carried out by deploying a support vector classifier, ET, LR, BNB, fully connected network, convolutional neural network, long short-term memory, and gated recurrent unit. Experimental results indicate that the stacked model performs better than both machine learning and deep learning models. With 74.01% accuracy, 70.84% precision, 75.65% recall, and 73.99% F1 score, the model outperforms the existing benchmark study.https://www.mdpi.com/2076-3417/12/20/10342threatening language detectionUrdu text classificationmachine learningstacking |
| spellingShingle | Aneela Mehmood Muhammad Shoaib Farooq Ansar Naseem Furqan Rustam Mónica Gracia Villar Carmen Lili Rodríguez Imran Ashraf Threatening URDU Language Detection from Tweets Using Machine Learning Applied Sciences threatening language detection Urdu text classification machine learning stacking |
| title | Threatening URDU Language Detection from Tweets Using Machine Learning |
| title_full | Threatening URDU Language Detection from Tweets Using Machine Learning |
| title_fullStr | Threatening URDU Language Detection from Tweets Using Machine Learning |
| title_full_unstemmed | Threatening URDU Language Detection from Tweets Using Machine Learning |
| title_short | Threatening URDU Language Detection from Tweets Using Machine Learning |
| title_sort | threatening urdu language detection from tweets using machine learning |
| topic | threatening language detection Urdu text classification machine learning stacking |
| url | https://www.mdpi.com/2076-3417/12/20/10342 |
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