Genetic algorithm based correlation enhanced prediction of online news popularity

Swati Choudhary, Angkirat Singh Sandhu, Tribikram Pradhan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Online News is an article which is meant for spreading awareness of any topic or subject published on the Internet and is available to a large section of users to gather information. For complete knowledge proliferation we need to know the right way and time to do so. For achieving this goal we have come up with a model which on the basis of, multiple factors, like describing the article type (structure and design) and publishing time predicts popularity of the article. In this paper we use Correlation techniques to get the dependency of the popularity obtained from an article, and then we use Genetic Algorithm to get the optimum attributes or best set which should be considered while formatting the article. Data has been procured from UCI Machine Learning Repository with 39644 articles with sixty condition attributes and one decision attribute. We implemented twelve different data learning algorithms on the above mentioned data set, including Correlation Analysis and Neural Network. We have also given a comparison of the performances got from various algorithms in the Result section.

Original languageEnglish
Title of host publicationComputational Intelligence in Data Mining - Proceedings of the International Conference on CIDM
PublisherSpringer Verlag
Pages133-144
Number of pages12
Volume556
ISBN (Print)9789811038730
DOIs
Publication statusPublished - 2017
Event3rd International Conference on Computational Intelligence in Data Mining, ICCIDM 2016 - Bhubaneswar, India
Duration: 10-12-201611-12-2016

Publication series

NameAdvances in Intelligent Systems and Computing
Volume556
ISSN (Print)2194-5357

Conference

Conference3rd International Conference on Computational Intelligence in Data Mining, ICCIDM 2016
CountryIndia
CityBhubaneswar
Period10-12-1611-12-16

Fingerprint

Learning algorithms
Learning systems
Genetic algorithms
Internet
Neural networks

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Choudhary, S., Sandhu, A. S., & Pradhan, T. (2017). Genetic algorithm based correlation enhanced prediction of online news popularity. In Computational Intelligence in Data Mining - Proceedings of the International Conference on CIDM (Vol. 556, pp. 133-144). (Advances in Intelligent Systems and Computing; Vol. 556). Springer Verlag. https://doi.org/10.1007/978-981-10-3874-7_13
Choudhary, Swati ; Sandhu, Angkirat Singh ; Pradhan, Tribikram. / Genetic algorithm based correlation enhanced prediction of online news popularity. Computational Intelligence in Data Mining - Proceedings of the International Conference on CIDM. Vol. 556 Springer Verlag, 2017. pp. 133-144 (Advances in Intelligent Systems and Computing).
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Choudhary, S, Sandhu, AS & Pradhan, T 2017, Genetic algorithm based correlation enhanced prediction of online news popularity. in Computational Intelligence in Data Mining - Proceedings of the International Conference on CIDM. vol. 556, Advances in Intelligent Systems and Computing, vol. 556, Springer Verlag, pp. 133-144, 3rd International Conference on Computational Intelligence in Data Mining, ICCIDM 2016, Bhubaneswar, India, 10-12-16. https://doi.org/10.1007/978-981-10-3874-7_13

Genetic algorithm based correlation enhanced prediction of online news popularity. / Choudhary, Swati; Sandhu, Angkirat Singh; Pradhan, Tribikram.

Computational Intelligence in Data Mining - Proceedings of the International Conference on CIDM. Vol. 556 Springer Verlag, 2017. p. 133-144 (Advances in Intelligent Systems and Computing; Vol. 556).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Choudhary S, Sandhu AS, Pradhan T. Genetic algorithm based correlation enhanced prediction of online news popularity. In Computational Intelligence in Data Mining - Proceedings of the International Conference on CIDM. Vol. 556. Springer Verlag. 2017. p. 133-144. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-981-10-3874-7_13