Analysis of Feature Reduction Techniques for Online News Popularity Prediction

Authors

  • Shivangi Bhargava P.G. Student Department of Computer Science and Engineering Maharana Pratap College of Technology Gwalior, India
  • Dr. Shivnath Ghosh Associate Professor Department of Computer Science and Engineering Maharana Pratap College of Technology Gwalior, India

DOI:

https://doi.org/10.24113/ijo-science.v4i10.165

Abstract

News popularity is the maximum growth of attention given for particular news article. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.

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References

Ilias N. Lymperopoulos, “Predicting the popularity growth of online content”, Elsevier, Vol. 369, pp. 585-613, 10 November 2016.

Kelwin Fernandes, Pedro Vinagre, Paulo Cortez, “A Proactive Intelligent Decision Support System for Predicting the Popularity of Online News”, Springer, EPIA 2015, pp. 535-546, 2015.

He Ren, Quan Yang, “Predicting and Evaluating the Popularity of Online News”, Standford University Machine Learning Report.

Bandari Roja, Sitaram Asur, and Bernardo A. Huberman. “The pulse of news in social media: Forecasting popularity.” arXiv preprint arXiv:1202.0332, 2012.

Ioannis Arapakis, B. Barla Cambazoglu, and Mounia Lalmas, “On the Feasibility of Predicting News Popularity at Cold Start”, Springer, pp. 290-299, 2014.

R. Shreyas, D.M Akshata, B.S Mahanand, B. Shagun, C.M Abhishek, “Predicting Popularity of Online Articles using Random Forest Regression”, International Conference on Cognitive Computing and Information Processing, IEEE, 2016

Swati Choudhary, Angkirat Singh Sandhu and Tribikram Pradhan, “Genetic Algorithm Based Correlation Enhanced Prediction of Online News Popularity” Computational Intelligence in Data Mining, Advances in Intelligent Systems and Computing, Springer, 2017, pp.133-144.

UCI Machine Learning Database, https://archive.ics.uci.edu/ml/datasets/Online+News+Popularity, May 2015.

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Published

10/13/2018

How to Cite

Bhargava, S., & Ghosh, D. S. (2018). Analysis of Feature Reduction Techniques for Online News Popularity Prediction. SMART MOVES JOURNAL IJOSCIENCE, 4(10), 11–17. https://doi.org/10.24113/ijo-science.v4i10.165