ONLINE NEWS CLASSIFICATION USING MACHINE LEARNING TECHNIQUES
A massive rise in web-based online content today pushes businesses to implement new approaches and resources that might support better navigation, processing, and handling of high-dimensional data. Over the Internet, 90% of the data is unstructured, and there are several approaches through which thi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IIUM Press, International Islamic University Malaysia
2021-07-01
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| Series: | International Islamic University Malaysia Engineering Journal |
| Subjects: | |
| Online Access: | https://journals.iium.edu.my/ejournal/index.php/iiumej/article/view/1662 |
| _version_ | 1818792492253839360 |
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| author | Jeelani Ahmed Muqeem Ahmed |
| author_facet | Jeelani Ahmed Muqeem Ahmed |
| author_sort | Jeelani Ahmed |
| collection | DOAJ |
| description | A massive rise in web-based online content today pushes businesses to implement new approaches and resources that might support better navigation, processing, and handling of high-dimensional data. Over the Internet, 90% of the data is unstructured, and there are several approaches through which this data can translate into useful, structured data—classification is one such approach. Classification of knowledge into a good collection of groups is significant and necessary. As the number of machine-readable documents proliferates, automatic text classification is badly needed to classify these documents. Unlabeled documents are categorized into predefined classes of labeled documents using text labeling, a supervised learning technique. This paper reviewed some existing approaches for classifying online news articles and discusses a framework for the automatic classification of online news articles. For achieving high accuracy, different classifiers were tried. Our experimental method achieved 93% accuracy using a Bayesian classifier and present in terms of confusion metrics.
ABSTRAK: Peningkatan tinggi pada masa kini pada maklumat dalam talian berasaskan web menyebabkan kaedah baru dalam bisnes telah diguna pakai dan sumber sokongan seperti navigasi, proses, dan pengurusan data berdimensi-tinggi adalah perlu. 90% data di internet adalah data tidak berstruktur, dan terdapat pelbagai kaedah data ini dapat diterjemahkan kepada data berguna, lebih berstruktur — iaitu melalui kaedah klasifikasi. Klasifikasi ilmu kepada koleksi kumpulan baik adalah penting dan perlu. Seperti mana mesin-boleh baca dokumen berkembang pesat, teks klasifikasi automatik juga sangat diperlukan bagi mengklasifikasi dokumen-dokumen ini. Dokumen yang tidak dilabel dikategori sebagai pengelasan pratakrif dokumen berlabel melalui teks label, iaitu teknik pembelajaran berpenyelia. Kajian ini mengkaji semula pendekatan sedia ada bagi artikel berita dalam talian dan membincangkan rangka kerja bagi pengelasan automatik artikel berita dalam talian. Bagi menghasilkan ketepatan yang tinggi, kami menggunakan pelbagai alat klasifikasi. Kaedah eksperimen ini mempunyai ketepatan 93% menggunakan pengelas Bayesian dan data dibentangkan berdasarkan matriks kekeliruan. |
| first_indexed | 2024-12-18T15:28:06Z |
| format | Article |
| id | doaj.art-64ba6c1cd35943e1b30397448c26c94d |
| institution | Directory Open Access Journal |
| issn | 1511-788X 2289-7860 |
| language | English |
| last_indexed | 2024-12-18T15:28:06Z |
| publishDate | 2021-07-01 |
| publisher | IIUM Press, International Islamic University Malaysia |
| record_format | Article |
| series | International Islamic University Malaysia Engineering Journal |
| spelling | doaj.art-64ba6c1cd35943e1b30397448c26c94d2022-12-21T21:03:13ZengIIUM Press, International Islamic University MalaysiaInternational Islamic University Malaysia Engineering Journal1511-788X2289-78602021-07-0122210.31436/iiumej.v22i2.1662ONLINE NEWS CLASSIFICATION USING MACHINE LEARNING TECHNIQUESJeelani Ahmed0Muqeem Ahmed1Maualan Azad National Urdu University, HyderabadSchool of Technology, Maulana Azad National Urdu University, HyderabadA massive rise in web-based online content today pushes businesses to implement new approaches and resources that might support better navigation, processing, and handling of high-dimensional data. Over the Internet, 90% of the data is unstructured, and there are several approaches through which this data can translate into useful, structured data—classification is one such approach. Classification of knowledge into a good collection of groups is significant and necessary. As the number of machine-readable documents proliferates, automatic text classification is badly needed to classify these documents. Unlabeled documents are categorized into predefined classes of labeled documents using text labeling, a supervised learning technique. This paper reviewed some existing approaches for classifying online news articles and discusses a framework for the automatic classification of online news articles. For achieving high accuracy, different classifiers were tried. Our experimental method achieved 93% accuracy using a Bayesian classifier and present in terms of confusion metrics. ABSTRAK: Peningkatan tinggi pada masa kini pada maklumat dalam talian berasaskan web menyebabkan kaedah baru dalam bisnes telah diguna pakai dan sumber sokongan seperti navigasi, proses, dan pengurusan data berdimensi-tinggi adalah perlu. 90% data di internet adalah data tidak berstruktur, dan terdapat pelbagai kaedah data ini dapat diterjemahkan kepada data berguna, lebih berstruktur — iaitu melalui kaedah klasifikasi. Klasifikasi ilmu kepada koleksi kumpulan baik adalah penting dan perlu. Seperti mana mesin-boleh baca dokumen berkembang pesat, teks klasifikasi automatik juga sangat diperlukan bagi mengklasifikasi dokumen-dokumen ini. Dokumen yang tidak dilabel dikategori sebagai pengelasan pratakrif dokumen berlabel melalui teks label, iaitu teknik pembelajaran berpenyelia. Kajian ini mengkaji semula pendekatan sedia ada bagi artikel berita dalam talian dan membincangkan rangka kerja bagi pengelasan automatik artikel berita dalam talian. Bagi menghasilkan ketepatan yang tinggi, kami menggunakan pelbagai alat klasifikasi. Kaedah eksperimen ini mempunyai ketepatan 93% menggunakan pengelas Bayesian dan data dibentangkan berdasarkan matriks kekeliruan.https://journals.iium.edu.my/ejournal/index.php/iiumej/article/view/1662Text ClassificationNaive BayesNews ClassificationSupport Vector MachineNews Articles |
| spellingShingle | Jeelani Ahmed Muqeem Ahmed ONLINE NEWS CLASSIFICATION USING MACHINE LEARNING TECHNIQUES International Islamic University Malaysia Engineering Journal Text Classification Naive Bayes News Classification Support Vector Machine News Articles |
| title | ONLINE NEWS CLASSIFICATION USING MACHINE LEARNING TECHNIQUES |
| title_full | ONLINE NEWS CLASSIFICATION USING MACHINE LEARNING TECHNIQUES |
| title_fullStr | ONLINE NEWS CLASSIFICATION USING MACHINE LEARNING TECHNIQUES |
| title_full_unstemmed | ONLINE NEWS CLASSIFICATION USING MACHINE LEARNING TECHNIQUES |
| title_short | ONLINE NEWS CLASSIFICATION USING MACHINE LEARNING TECHNIQUES |
| title_sort | online news classification using machine learning techniques |
| topic | Text Classification Naive Bayes News Classification Support Vector Machine News Articles |
| url | https://journals.iium.edu.my/ejournal/index.php/iiumej/article/view/1662 |
| work_keys_str_mv | AT jeelaniahmed onlinenewsclassificationusingmachinelearningtechniques AT muqeemahmed onlinenewsclassificationusingmachinelearningtechniques |