Exploration of Text Classification Approach to Classify News Classification

  • Pawan Kumar Verma


From a few decades, there has been an increase in the advancement of text classification techniques. Text classification tools are required to carry out labeling and retrieval of this rapidly growing textual content. This research paper compares four very prominent algorithms for news classification which include Naïve Bayes, SVM, Random Forest and MLP Classifier. In comparison with the other approaches, Naïve Bayes is likely to be a better approach to serving as a text classification model due to its homogeneity. This paper proposed the news classification comparing all four classifiers in which several types of different news has been classified like business & finance, sports, politics & policy, Criminal justice, and health.

Keywords: Text Classification, News Classification, Naïve Bayes, SVM, Random Forest, MLP Classifier.

How to Cite
Pawan Kumar Verma. (2020). Exploration of Text Classification Approach to Classify News Classification. International Journal of Advanced Science and Technology, 29(05), 2555 - 2562. Retrieved from http://sersc.org/journals/index.php/IJAST/article/view/11150