Advertisement

Performance Evaluation of Meta-Heuristic Algorithms in Social Media Using Twitter

  • P. Silambarasi
  • Kiran L. N. ErankiEmail author
Conference paper
  • 29 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1087)

Abstract

Internet has opened avenues for social presence ubiquitously. Resulting in sharing of opinions, sentiments and reviews across various social media platforms such as Twitter, Facebook, Instagram creating practical demands and research challenges. This paper presents a review on performance evaluation of meta-heuristic algorithms in social media using opinion tweeted data set. We begin with generalized view of meta-heuristic algorithms. And then, we investigate the differences among cuckoo search, KNN and other meta-heuristic algorithms. Followed by comparative analysis of performance when applied on Twitter dataset. Finally, we conclude by discussing some challenges and open problems related to application of meta-heuristic algorithms in social network analysis.

Keywords

Cuckoo search KNN Meta-Heuristic algorithms Twitter 

References

  1. 1.
    N.H.T. An, V.N. Dieu, T.T. Nguyen, V.T. Kien, One rank cuckoo search algorithm for optimal reactive power dispatch. GMSARN Int. J. 73 (2015)Google Scholar
  2. 2.
    R. Bose, R.K. Dey, S. Roy, D. Sarddar, Analyzing political sentiment using twitter data, in Information and Communication Technology for Intelligent Systems (Springer, Berlin, 2019), pp. 427–436Google Scholar
  3. 3.
    M. Dhivya, M. Sundarambal, L.N. Anand, Energy efficient computation of data fusion in wireless sensor networks using cuckoo based particle approach (CBPA). Int. J. Commun. Network Syst. Sci. 4(04), 249 (2011)Google Scholar
  4. 4.
    A.H. Halim, I. Ismail, Combinatorial optimization: Comparison of heuristic algorithms in travelling salesman problem. Arch. Comput. Methods Eng. 26(2), 367–380 (2019)MathSciNetCrossRefGoogle Scholar
  5. 5.
    K. Huang, Y. Zhou, X. Wu, Q. Luo, A cuckoo search algorithm with elite opposition-based strategy. J. Intell. Syst. 25(4), 567–593 (2016)Google Scholar
  6. 6.
    A. Layeb, A novel quantum inspired cuckoo search for knapsack problems. Int. J. Bio-inspired Comput. 3(5), 297–305 (2011)CrossRefGoogle Scholar
  7. 7.
    A. Ouaarab, B. Ahiod, X.S. Yang, Discrete cuckoo search algorithm for the travelling salesman problem. Neural Comput. Appl. 24(7–8), 1659–1669 (2014)CrossRefGoogle Scholar
  8. 8.
    A.C. Pandey, D.S. Rajpoot, M. Saraswat, Twitter sentiment analysis using hybrid cuckoo search method. Inf. Process. Manage. 53(4), 764–779 (2017)CrossRefGoogle Scholar
  9. 9.
    R. Rajabioun, Cuckoo optimization algorithm. Appl. Soft Comput. 11(8), 5508–5518 (2011)CrossRefGoogle Scholar
  10. 10.
    R. Tang, S. Fong, X.S. Yang, S. Deb, Integrating nature-inspired optimization algorithms to k-means clustering, in Seventh International Conference on Digital Information Management (ICDIM 2012) (IEEE, New York, 2012), pp. 116–123Google Scholar
  11. 11.
    X.S. Yang, S. Deb, Cuckoo search via Lévy flights, in 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC) (IEEE, New York, 2009), pp. 210–214Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of ComputingSASTRA Deemed UniversityThanjavurIndia

Personalised recommendations