Online Writeup Categorization using a Machine Learning Strategy

Prof. Dipti A. Mirkute; Prasad D. Banarase

doi:10.32628/IJSRSET24115118

Authors

Prof. Dipti A. Mirkute Yavatmal, Maharashtra, India Author
Prasad D. Banarase Yavatmal, Maharashtra, India Author

DOI:

https://doi.org/10.32628/IJSRSET24115118

Keywords:

News Articles, Machine Learning, Classification of News, Support Vector Machine, Logistic Regression

Abstract

A variety of online sources produces a huge amount of daily news; thus, it is important to categorize the news items to make the information accessible to consumers easily and quickly scraping is used to gather existing news items from news websites and then categorize them automatically using a variety of classification algorithms. As a result, news categorization is a technique for discovering untracked news themes and providing specific recommendations based on the user's historical interests. The BBC News dataset, which includes articles from five different categories including Business, Entertainment, Politics, Sport, and Technology, is used in this task to discuss various steps in news classification and implement a few algorithmic approaches such as Naive Bayes, Binary Classifier, SVM, Perceptron, and SGD. In the study, results from several classification algorithms are examined, and their accuracy is measured.

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