Newspaper Article Classification using Machine Learning Techniques
J SreeDevi1, M Rama Bai2, M Chandrashekar Reddy3
1J Sree Devi*, CSE, MGIT,JNTUH, Hyderabad, India.
2Dr M Rama Bai, CSE,MGIT,JNTUH, Hyderabad, India.
3Mr M Chandrashekar Reddy, Student, CSE, MGIT, Hyderabad, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 21, 2020. | Manuscript published on March 10, 2020. | PP: 872-877 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2753039520/2020©BEIESP | DOI: 10.35940/ijitee.E2753.039520
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Newspaper articles offer us insights on several news. They can be one of many categories like sports, politics, Science and Technology etc. Text classification is a need of the day as large uncategorized data is the problem everywhere. Through this study, We intend to compare several algorithms along with data preprocessing approaches to classify the newspaper articles into their respective categories. Convolutional Neural Networks(CNN) is a deep learning approach which is currently a strong competitor to other classification algorithms like SVM, Naive Bayes and KNN. We hence intend to implement Convolutional Neural Networks – a deep learning approach to classify our newspaper articles, develop an understanding of all the algorithms implemented and compare their results. We also attempt to compare the training time, prediction time and accuracies of all the algorithms.
Keywords: Newspaper Articles, CNN, SVM, Naïve Bayes, KNN
Scope of the Article: Machine Learning