CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Optimizing Extreme Learning Machine using GWO Algorithm for Sentiment Analysis

by Mustafa Abdul Salam, Mahmoud Ali
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 38
Year of Publication: 2020
Authors: Mustafa Abdul Salam, Mahmoud Ali
10.5120/ijca2020920450

Mustafa Abdul Salam, Mahmoud Ali . Optimizing Extreme Learning Machine using GWO Algorithm for Sentiment Analysis. International Journal of Computer Applications. 176, 38 ( Jul 2020), 22-28. DOI=10.5120/ijca2020920450

@article{ 10.5120/ijca2020920450,
author = { Mustafa Abdul Salam, Mahmoud Ali },
title = { Optimizing Extreme Learning Machine using GWO Algorithm for Sentiment Analysis },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2020 },
volume = { 176 },
number = { 38 },
month = { Jul },
year = { 2020 },
issn = { 0975-8887 },
pages = { 22-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number38/31451-2020920450/ },
doi = { 10.5120/ijca2020920450 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:44:31.587373+05:30
%A Mustafa Abdul Salam
%A Mahmoud Ali
%T Optimizing Extreme Learning Machine using GWO Algorithm for Sentiment Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 38
%P 22-28
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Sentiment analysis on social media is one of the most popular text mining application and many researchers have devoted more efforts in this interesting field. Sentiment analysis is a method for analyzing data and extracting the feeling it represents. Twitter is considered one of the most common social media forums used by people on various occasions to express their opinions and express feelings. Twitter's sentiment analysis has grown gradually over the past few decades. Due to the format of small tweet, a new dimension is created for problems such as slang usage, abbreviations, etc. This paper proposes a hybrid approach that optimizes extreme learning machine (ELM) classifier with one of the most recent swarm intelligence algorithms which is grey wolf optimization algorithms (GWO). GWO is used to self-adaptation of hidden neurons weights rather than manually selection. Also avoid the overfitting problem and make the model more generalized and robust. Results represented in this paper showed that the proposed hybrid model GWO-ELM overcame the problems found in classical ELM model and achieved best accuracy against all compared models.

References
  1. A. Cobo, D. Parra, and J. Navon. Identifying relevant messages in a twitter-based ´ citizen channel for natural disaster situations. In Proceedings of the 24th International Conference on World Wide Web Companion, pages 1189–1194, 2015.
  2. E. Boiy, P. Hens, K. Deschacht and M. Moens, 'Automatic sentiment analysis in on-line text', 11th International Conference on Electronic Publishing, vol. 349360, 2007.
  3. D. Zimbra, M. Ghiassi and S. Lee, "Brand-Related Twitter Sentiment Analysis using Feature Engineering and the Dynamic Architecture for Artificial Neural Networks", IEEE 1530-1605, 2016.
  4. O. Hegazy, O.S. Soliman, and M. Abdul Salam, "A Machine Learning Model for Stock Market Prediction", International Journal of Computer Science and Telecommunications, Vol. (4), Issue (12),pp. 17-23, December 2013.
  5. O. Hegazy, O.S. Soliman, and M. Abdul Salam, "LSSVM-ABC Algorithm for Stock Price Prediction", International Journal of Computer Trends and Technology (IJCTT), Vol. (7), Issue (2),pp. 81-92, Jan 2014.
  6. O. Hegazy, O.S. Soliman, and M. Abdul Salam, "Optimizing LS-SVM using Modified Cuckoo Search algorithm (MCS) for Stock Price Prediction", International Journal of Advanced Research in Computer Science and Management Studies, Vol. (3), Issue (2),pp. 204-224, February 2015.
  7. O. Hegazy, O.S. Soliman, and M. Abdul Salam, "Comparative Study between FPA, BA, MCS, ABC, and PSO Algorithms in Training and Optimizing of LS-SVM for Stock Market Prediction", International Journal of Advanced Computer Research Vol.(5), Issue (18),pp.35-45, March-2015.
  8. O. Hegazy, O.S. Soliman, and M. Abdul Salam, "FPA-ELM Model for Stock Market Prediction", International Journal of Advanced Research in Computer Science and Software Engineering, Vol.(5), Issue (2),pp.1050-1063, February 2015.
  9. R. Salem, M. Abdul Salam, H. Abdelkader and A. Awad Mohamed, "An Artificial Bee Colony Algorithm for Data Replication Optimization in Cloud Environments," in IEEE Access, vol. 8, pp. 51841-51852, 2020, doi: 10.1109/ACCESS.2019.2957436.
  10. EM Badr, M. Abdul Salam, M Ali, H Ahmed, Social Media Sentiment Analysis using Machine Learning and Optimization Techniques, International Journal of Computer Applications (0975 – 8887) Volume 178 – No. 41, August 2019.
  11. J. Leskovec and J.J. Mcauley. 2012. Learning to discover social circles in ego networks. In Advances in neural information processing systems. 539–547.
  12. S. Schifferes, N. Newman, N. Thurman, verifying news through social media: Developing a user-centred tool for professional journalists. Digital Journalism 2, 3 (2014), 406–418.
  13. A. Ceron, L. Curini, S.M Iacus, and G. Porro. 2014. Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens’ political preferences with an application to Italy and France. New Media & Society 16, 2 (2014), 340–358.
  14. AP. Engelbrecht (2006), Fundamentals of computational swarm intelligence. Wiley, Hoboken.
  15. M. Dorigo, LM. Gambardella (1997), Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66.
  16. S. Mirjalili, S.M. Mirjalili, and A. Lewis, Grey wolf optimizer. Advances in Engineering Software, 69:46–61, 2014.
  17. R. Eberhart and J. Kennedy, A new optimizer using particle swarm theory. In Micro Machine and Human Science, 1995. MHS’95., Proceedings of the Sixth International Symposium on, pages 39–43. IEEE, 1995.
  18. E. Emary K.K.A. Ghany B. Parv M.A. Salam, H.M. Zawbaa, A hybrid dragonfly algorithm with extreme learning machine for prediction. International Symposium on INnovations in Intelligent SysTems and Applications (INISTA), pages 1–6, 2016.
  19. P. Dass, H. Sharma, J.C. Bansal, and K.E. Nygard, Meta heuristics for prime factorization problem. In Nature and biologically inspired computing (NaBIC), 2013 World Congress on, pages 126–131. IEEE, 2013.
  20. M. Mishra, U. Chaturvedi, and S.K. Pal, A multithreaded bound varying chaotic firefly algorithm for prime factorization. In Advance Computing Conference (IACC), 2014 IEEE International, pages 1322–1325. IEEE, 2014.
  21. EC Laskari, GC Meletiou, and MN Vrahatis. Problems of cryptography as discrete optimization tasks. Nonlinear Analysis: Theory, Methods & Applications, 63(5):e831–e837, 2005.
  22. M. Abdul Salam, H.M. Abdel Kader, " Evaluation of Differential Evolution and Particle Swarm Optimization Algorithms at Training of Neural Network for Stock Prediction", Int. Arab. J. e Technol. 2 (3), 145-151, 2012.
  23. A Gupta, J.Pruthi, N.Sahu" Sentiment Analysis of Tweets using Machine Learning Approach " International Journal of Computer Science and Mobile Computing, Vol.6 Issue.4, April- 2017, pg. 444-458.
  24. L. B, J. Feng. “Robust Sentiment Detection on Twitter from Biased and Noisy Data". COLING 2010: Poster Volume,pp. 36-44.
  25. B. Naiknaware, B. Kushwaha, S. Kawathekar , “Social Media Sentiment Analysis using Machine Learning Classifiers" International Journal of Computer Science and Mobile Computing, Vol.6 Issue.6, June- 2017, pg. 465-472.
  26. A. Bifet and E. Frank, "Sentiment Knowledge Discovery in Twitter Streaming Data", In Proceedings of the 13th International Conference on Discovery Science, Berlin, Germany: Springer,2010, pp. 1-15.
  27. A. Andrew, "An Introduction to Support Vector Machines and Other Kernel‐based Learning Methods20016Nello Christianini and John Shawe‐Taylor. An Introduction to Support Vector Machines and Other Kernel‐based Learning Methods. Cambridge University Press, 2000. xiii + 189 pp., ISBN: ISBN 0‐521‐78019‐5 Hardback:£27.50", Kybernetes, vol. 30, no. 1, pp. 103-115, 2001.
  28. E. AL-Daoud, “Integration of Support Vector Machine and Bayesian Neural Network for Data Mining and Classification”, World Academy of Science, Engineering and Technology, vol. 64, 2010.
  29. A. Go, R. Bhayani, L.Huang. “Twitter Sentiment Classification Using Distant Supervision". Stanford University, Technical Paper,2009.
  30. M. Shad Akhtar ,D. Gupta ,A. Ekbal & Bhattacharyya, P. (2017). Feature selection and ensemble construction: A twostep method for aspect based sentiment analysis. KnowledgeBased Systems, 125, 116-135.
  31. H. Kang, SJ. Yoo and D. Han, ” Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews”, Expert Systems with Applications, Elsevier, vol. 39, no. 5, pp. 6000—6010, 2012.
  32. M. Koppel and j. Schler, "the importance of neutral examples for learning sentiment", computational intell, vol. 22, no. 2, pp. 100-109, 2006.
  33. D. Romero, B. Meeder and J. Kleinberg, "Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on Twitter", Proceedings of the 20th international conference on World wide web, pp. 695--704, 2011.
  34. M. Anjaria and R. Guddeti, "Influence factor based opinion mining of Twitter data using supervised learning", Communication Systems and Networks (COMSNETS), 2014 Sixth International Conference on, pp. 1--8, 2014.
  35. J. Martineau and T. Finin, “Delta TFIDF: An Improved Feature Space for Sentiment Analysis”, Proceedings of the Third International ICWSM Conference, vol. 9, 2009.
  36. SM. Mohammad, S. Kiritchenko and X. Zhu, “NRC-Canada: Building the State-of- the-Art in Sentiment Analysis of Tweets”, arXiv preprint arXiv:1308.6242, 2013.
  37. P. Barnaghi, J.G. Breslin and P. Ghaffari, “Opinion Mining and Sentiment Polarity on Twitter and Correlation between Events and Sentiment”, 2016 IEEE Second International Conference on Big Data Computing Service and Applications.
  38. B.S. Halima and S Chitrakala, “Trending Topic Analysis Using Novel Sub Topic Detection Model”, (IEEE) ISBN- 978-1-4673-9745-2, 2016.
  39. S. Yuan, J. Wu, L. Wang and Q. Wang, “A Hybrid Method for Multi-class Sentiment Analysis of Micro-blogs”, ISBN- 978-1-5090-2842-9, 2016.
  40. P.N. Suganthan G.B. Huang Q.Y. Zhu, A.K. Qin. Evolutionary extreme learning machine. Pattern Recognition, 38(10):1759–1763, 2005.
  41. L. Zhiping C. Jiuwen. Extreme learning machines on high dimensional and large data applications: A survey. Mathematical Problems in Engineering, 2015(1):1–13, 2015.
  42. C.K. Siew G.B. Huang, Q.Y. Zhu. Extreme learning machine: theory and applications. Neurocomputing, 70(1):489–501, 2006.
Index Terms

Computer Science
Information Sciences

Keywords

Sentiment Analysis Machine Learning Extreme Learning Machine Grew Wolf Optimization Text Mining.

Powered by PhDFocusTM