Fake News Accuracy using Naive Bayes Classifier
R.J. Poovaraghan1, M.V. Keerti Priya2, P.V. Sai Surya Vamsi3, Mansi Mewara4, Sowmya Loganathan5
1R.J. Poovaraghan, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
2M.V. Keerti Priya, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
3P.V. Sai Surya Vamsi, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
4Mansi Mewara, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
5Sowmya Loganathan, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
Manuscript received on 18 June 2019 | Revised Manuscript received on 11 July 2019 | Manuscript Published on 17 July 2019 | PP: 962-964 | Volume-8 Issue-1C2 May 2019 | Retrieval Number: A11660581C219/2019©BEIESP
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© 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: This paper helps us to detect the accuracy of the fake news using Naive Bayes classification. Here the data is divided into test dataset and train dataset and the train dataset is divided into groups of similar information. Test data is later matched with these groups and accuracy is found using Naive Bayes classifier. It helps in knowing whether a given news is fake or real. It provides maximum accuracy and helps to determine the fake news.
Keywords: Machine Learning, Fake News Classification, Probability.
Scope of the Article: Machine Learning