CFP last date
20 January 2025
Reseach Article

Ensemble based Hybrid Machine Learning Approach for Sentiment Classification- A Review

by Rabi Narayan Behera, Manan Roy, Sujata Dash
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 146 - Number 6
Year of Publication: 2016
Authors: Rabi Narayan Behera, Manan Roy, Sujata Dash
10.5120/ijca2016910813

Rabi Narayan Behera, Manan Roy, Sujata Dash . Ensemble based Hybrid Machine Learning Approach for Sentiment Classification- A Review. International Journal of Computer Applications. 146, 6 ( Jul 2016), 31-36. DOI=10.5120/ijca2016910813

@article{ 10.5120/ijca2016910813,
author = { Rabi Narayan Behera, Manan Roy, Sujata Dash },
title = { Ensemble based Hybrid Machine Learning Approach for Sentiment Classification- A Review },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 146 },
number = { 6 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 31-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume146/number6/25405-2016910813/ },
doi = { 10.5120/ijca2016910813 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:49:42.272438+05:30
%A Rabi Narayan Behera
%A Manan Roy
%A Sujata Dash
%T Ensemble based Hybrid Machine Learning Approach for Sentiment Classification- A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 146
%N 6
%P 31-36
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Due to digital explosion, huge amount of data is generated from different sources which require critical analysis for decision making. In recent days one of the challenging issues like sentiment classification has drawn the attention of many researchers working in the area of opinion mining. The supervised machine learning technique is used for analyzing sentiments associated with unstructured text data. But, recently it has been observed from the findings that ensemble based learning algorithm achieves better understanding and acceptance of the solution in terms of diversity and accuracy. In this paper, an extensive study of ensemble based machine learning techniques in the domain of sentiment classification has been done to enhance the efficiency, by adopting multiple learning algorithms to obtain better predictive performance, that would be obtained from any of the constituent learning algorithms. Again, how the analysis will become stronger, some suggestions are proposed at the end of the discussion.

References
  1. Walaa Medhat, Ahmed Hassan, Hoda Korashy (2014). Sentiment Analysis algorithms and applications: A survey. Ain Shams Engineering Journal, Elsevier, Volume 5,Issue 4,pp. 1093-1113
  2. Melville, Wojciech Gryc (2009).Sentiment Analysis of Blogs by Combining Lexical Knowledge with Text Classification.ACM.
  3. Hanhoon Kang, Seong Joon Yoo, Dongil Han(2012).Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews. Expert Systems with Applications.
  4. Rui Xia, Chengqing Zong, Shoushan Li (2011).Ensemble of feature sets and classification algorithms for sentiment classification. Information Sciences.
  5. M.RushdiSaleh,M.T.MartínValdivia,RáezL.A.Urena-Lopez,A.Montejo(2011).Experiments with SVM to classify opinions in different domains. Expert Systems with Applications.
  6. Kaufmann JM. JMax Align: A Maximum Entropy Parallel Sentence Alignment Tool. In: Proceedings of COLING’12:Demonstration Papers, Mumbai; 2012. p. 277–88.
  7. website::http://publications.drdo.gov.in/ojs/index.php/dsj/article/view/1088/4752
  8. David Opitz, Richard Maclin (1999).Popular Ensemble Methods: An Empirical Study Journal of Artificial Intelligence Research pp.169-198.
  9. Antonio Torralba, Kevin P.Murphy, William T. Freeman (2004).Sharing features: Efficient boosting procedures for multiclass object detection.MIT, Cambridge
  10. Martin Sewell (2007).Ensemble Learning. Research Note. RN/11/02.
  11. Anwar,H.,Qamar, U.,& Muzaffar Qureshi, A.W(2014). Global Optimization Ensemble Model for Classification Methods. The Scientific World Journal, Hindawi Publishing Corporation.
  12. Zeng, B., Luo, Z., & Wei, J (2008).Sea Water Pollution Assessment Based on Ensemble of Classifiers.In Natural Computation, ICNC'08.
  13. Fourth International Conference on Vol. 1, pp. 241-245.IEEEWang,W (2010).Heterogeneous Bayesian Ensembles for Classifying Spam Emails. The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1-8,IEEE.
  14. A. Rahman and B. Verma (2011).Ensemble Classifier Composition: Impact on Feature Based Offline Cursive Character Recognition, Proc. IEEE International Joint Conference on Neural Networks (IJCNN), San Jose, USA.
  15. Ying Su,Yong Zhang, Donghong Ji, Yibing Wan,Hongmiao Wu(2012).Ensemble learning for Sentiment Classification. Springer.
  16. Gang Wang,Jianshan Sun,Jian Ma, Kaiquan Xu,Jibao Gu(2013). Sentiment classification: The contribution of ensemble learning.Elsevier
  17. E. Fersini, E. Messina, F.A. Pozzi (2014). Sentiment analysis:Bayesian Ensemble Learning. Elsvier.
  18. Bharathidason,S., & Jothi Venkataeswaran, C (2014).Improving Classification Accuracy based on Random Forest Model with Uncorrelated High Performing Trees .Interna-tional Journal of Computer Applications, vol. 101, issue 13, pp. 26-30.
  19. M.Govindarajan (2014).Bagged Ensemble Classifiers for Sentiment Classification of Movie Reviews. IJECS Volume 3.Issue 2.
  20. Matthias Hagen, Martin Potthast, Michel Büchner, Benno Stein(2015).Webis:An Ensemble for Twitter Sentiment Detection. In the proceedings of the 9th International Workshop on Semantic Evaluation held on June 4-5 pp. 582–589.Denver,Colorado
  21. Yongjun Piao, Minghao Piao, Cheng Hao Jin, Ho Sun Shon, Ji Moon Chung, Buhyun Hwang, Keun Ho Ryu (2015).A New Ensemble Method with Feature Space Partitioning for High-Dimensional Data Classification. Mathematical Problems in Engineering, Article ID 590678
  22. Junyi Xu, Li Yao, Le Li (2015). Argumen -tation Based Joint Learning:A Novel Ensemble Learning Approach.Plos One.
  23. Amine Bayoudhi,Lamia Hadrich Belguith,Hatem Ghorbel (2015).Sentiment Classification of Arabic Documents: Experiments with multi-type features and ensemble algorithms. In the proceedings of 29th Pacic Asia Conference on Language, Information and Computation held on October30-November1, pp.196–205, Shanghai, China.
  24. Xueyi Wang(2012). A new model for measuring the accuracies of majority voting ensembles,In proceedings of IEEE International Joint Conference on Neural Networks (IJCNN), Brisbane, OLD
  25. Maher Ala'raj, Maysam Abbod (2015).A systematic credit scoring model based on heterogeneous classifier ensembles, In the proccedings of the IEEE 2015 International Symposium onInnovations in Intelligent Systems and Applications (INISTA), Madrid
  26. Suresh Ramakrishnan, Maryam Mirzaei, Mahmoud Bekri(2014). Adaboost Ensemble Classifiers for Corporate Default Prediction,In the Proceeding of 1st International Conference of Recent Trends in Information and Communication Technologies, Johor, Malaysia
  27. Gang Wang, Jian Ma (2011).Study of corporate credit risk prediction based on integrating boosting and random subspace.Expert Systems with Applications, volume-38, issue-11, pp. 13871-13878
Index Terms

Computer Science
Information Sciences

Keywords

Sentiment Analysis Ensemble learning Classification Random Subspace Bagging Boosting