Unravelling Modern News Classification Methods: A Systematic Review

Authors

  • Aarushi Gupta
  • Pulkit Sharma Maharaja Agrasen Institute of Technology
  • K. C. Tripathi Maharaja Agrasen Institute of Technology
  • M. L. Sharma Maharaja Agrasen Institute of Technology

DOI:

https://doi.org/10.37591/jocta.v12i3.864

Keywords:

news classification, machine learning, deep learning, data preprocessing

Abstract

Nowadays, the news is being generated each second from every corner of the world, with millions of news articles generated every day. Some assume that at least 1.8 million articles are published yearly, in about 28,000 journals. It has become difficult to recognize what's fake and what's genuine due to the overflow of millions of articles every day. Not every person reads every news, so the classification of news according to people’s interests has become significant. 80% of the information is unstructured and “text” being the most common type of unstructured data has led to the emergence of numerous news classification methods. Some of the modern news classification methods stretch from using Machine Learning models like “SVM”, “Naive Bayes”, “Decision Tree” to the modern complex transformer and deep learning models like “BERT” and “LSTM”. So, the main aim of this review is to better understand the pipeline and shortcomings of the news classification processes aforementioned. This article will help researchers to distinguish between various models and choose the one best for their projects.

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Published

2022-01-06

Issue

Vol. 12 No. 3 (2021)

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Articles

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