Online News Classification Using Machine Learning Techniques

Abstract

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.

Comparative Study for News Categorization by Multinomial Naïve Bayes and Support Vector Machine

“…Some well-known classifier evaluation measures include accuracy (also known as recognition rate), sensitivity (or recall), specificity, precision, and F1 Score [7], [19], [20].…”

“…It is a difficult process to find news relevant to a person's interests because many news articles are useful but may be irrelevant. News articles are categorized by news type/category for easy selection of people according to their likes, news and the type of news may affect the attractiveness of an individual [7].…”

UTAPIS Indonesian Word Error Detection Application: Design and Development

“…The algorithm used to detect spelling errors in the word teruah is Jaccard Similarity; this algorithm compares two pieces of data, which are then calculated using a formula called the Jaccard Index. The first data is in the form of articles received from the U-U-Tapis Web [15].…”

News Classification by N-Gram and Machine Learning Algorithms

“…News articles are categorized by news type/category for easy selection of people according to their likes. News and the type of news, may affect the attractiveness of an individual [7].…”