An Empirical Study of the Extreme Learning Machine for Twitter Sentiment Analysis

  • Onder Coban Cukurova University
  • Buse Melis Ozyildirim Cukurova University
  • Selma Ayse Ozel Cukurova University
Keywords: Twitter, Sentiment Analysis, Support Vector Machine, Extreme Learning Machine


Extreme Learning Machine (ELM) method is proposed for single hidden layer feed-forward networks (SLFNs). The ELMemploys feed-forward neural network architecture and works with randomly determined input weights. In this aspect, ELM depends onprinciple that enables to determine weights and biases in the network. In the first phase of ELM that can be named as feature mapping,the usage of random values differs the ELM from other methods that employ a kernel function for feature mapping such as SupportVector Machines (SVM) and Deep Neural Networks. After the feature mapping, the main goal of the ELM is to learn weights betweenhidden and output layers by minimizing the error. The ELM has gained much more popularity recently; and can be utilized forclassification, regression, and dimension reduction. In literature, Twitter sentiment analysis is generally considered as a classificationtask. Therefore, in this study, the basic ELM is utilized for Twitter sentiment analysis and compared with the SVM which is one of themost successful machine learning algorithms used for sentiment analysis. Experiments are conducted on two different Turkish datasets.Experimental results show that the performance of the two methods are slightly different, but SVM outperforms basic ELM.


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Author Biographies

Onder Coban, Cukurova University
Faculty of Engineering Architecture, Department of Computer Engineering
Buse Melis Ozyildirim, Cukurova University
Faculty of Engineering Architecture, Department of Computer Engineering
Selma Ayse Ozel, Cukurova University
Faculty of Engineering Architecture, Department of Computer Engineering


S. Sommer et al., “Analyzing customer sentiments in microblogs–A topic-model-based approach for Twitter datasets,” In Proceedings of the Americas Conference on Information Systems, Detroit, USA, 2011.

M. Michelson and S. A. Macskassy, “Discovering users’ topics of interest on twitter: a first look,” In Proceedings of the fourth workshop on Analytics for noisy unstructured text data, Toronto, Canada, 2010, pp. 73-80.

A Survey of Opinion Mining and Sentiment Analysis, B. Liu and L. Zhang, In Mining Text Data, C. Aggarwal, C. Zhai, eds, Boston, MA, Springer, Boston, 2012.

Twitter, 2016; Available from:

A. Giachanou and F. Crestani, “Like it or not: A survey of twitter sentiment analysis methods,” ACM Computing Surveys., vol. 49, no. 2, 2016, Art. no. 28.

G. Huang et al., “Trends in extreme learning machines: a review,” Neural Networks, vol. 61, pp. 32-48, 2015.

G. B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, vol. 70, no. 1, pp. 489-501, 2006.

T. Joachims, “Text categorization with support vector machines: Learning with many relevant features,” In 10th European Conference on Machine Learning, Chemnitz, Germany, 1998, pp. 137-142.

G. B. Huang, D. H. Wang, and Y. Lan, “Extreme learning machines: a survey,” International Journal of Machine Learning and Cybernetics., vol. 2, no. 2, pp. 107-122, 2011.

G. Huang et al., “Semi-supervised and unsupervised extreme learning machines,” IEEE transactions on cybernetics, vol. 4, no. 12, pp. 2405-2417, 2014.

F. BenoíT et al., “Feature selection for nonlinear models with extreme learning machines,” Neurocomputing, vol. 102, pp. 111-124, 2013.

S. Poria et al., “Sentic patterns: Dependency-based rules for concept-level sentiment analysis,” Knowledge-Based Systems, vol. 69, pp. 45-63, 2014.

E. Cambria et al., “An ELM-based model for affective analogical reasoning,” Neurocomputing, vol. 149, pp. 443-455, 2015.

W. Zheng, Y. Qian, and H. Lu, “Text categorization based on regularization extreme learning machine,” Neural Computing and Applications, vol. 22, no. 3-4, pp. 447-456, 2013.

X. G. Zhao et al., “XML document classification based on ELM,” Neurocomputing, vol. 74, no. 16, pp. 2444–2451, 2011.

X. Wang et al., “A depression detection model based on sentiment analysis in micro-blog social network,” In Pacific-Asia Conference on Knowledge Discovery and Data Mining, Berlin, Heidelberg, 2013, pp. 201-213.

M. Çetin and M. F. Amasyali, “Supervised and traditional term weighting methods for sentiment analysis,” In Signal Processing and Communications Applications Conference, Haspolat, Turkey, 2013, pp. 1-4.

A. Hayran and M. Sert, “Sentiment analysis on microblog data based on word embedding and fusion techniques,” In Signal Processing and Communications Applications Conference, Antalya, Turkey, 2017, pp. 1-4.

O. Coban, B. Ozyer, and G. T. Ozyer, “A Comparison of Similarity Metrics for Sentiment Analysis on Turkish Twitter Feeds,” In IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity), Chengdu, China, 2015, pp. 333-338.

A. Go, R. Bhayani, and L. Huang, “Twitter sentiment classification using distant supervision,” Stanford, CA, USA, CS224N Project Report, 2009.

Apache. Lucene. Available from:

A. A. Akın and M. D. Akın, “Zemberek, an open source nlp framework for turkic languages,” Structure, vol. 10, 2007.

C. Whitelaw, N. Garg, and S. Argamon, “Using appraisal groups for sentiment analysis,” In Proceedings of the 14th ACM international conference on Information and knowledge management, Bremen, Germany, 2005, pp. 625-631.

I. Kanaris et al., “Words vs. character n-grams for anti-spam filtering,” International Journal on Artificial Intelligence Tools, pp. 1-20, 2006.

H. Lodhi et al., “Text classification using string kernels,” Journal of Machine Learning Research, vol. 2, pp. 419-444, 2002.

O. Coban and G. T. Ozyer, “The impact of term weighting method on Twitter sentiment analysis,” Pamukkale Univ Muh Bilim Derg., to be published. DOI: 10.5505/pajes.2016.50480.

G. Salton and C. Buckley, “Term-weighting approaches in automatic text retrieval,” Information processing & management, vol. 24, no. 5, pp. 513-523, 1988.

G. B. Huang and H.A. Babri, “Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions,” IEEE Trans. Neural Networks., vol. no. 1, pp. 224–229, 1998.

Y. W. Huang and D. H. Lai, “Hidden node optimization for extreme learning machine,” Aasri Procedia., vol. 3, pp. 375–380, 2012.

S. Xu, J. Wang, “A fast incremental extreme learning machine algorithm for data streams classification,” Expert Systems with Applications., vol. 65, pp. 332-344, 2016.

G. B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: a new learning scheme of feedforward neural networks,” In Proceedings of the IEEE International Joint Conference on Neural Networks, Budapest, Hungary, 2004, pp. 985-990.

A. Gosso, and M. A. Gosso, “Package ‘elmNN’,” ELM Package Version 1.0, July. 17, 2012. [Online]. Available:

Burges, C. J. (1998). A tutorial on support vector machines for pattern recognition. Data mining and knowledge discovery, 2(2), 121-167.

D. Fradkin and I. Muchnik, “Support vector machines for classification,” DIMACS Series in Discrete Mathematics and Theoretical Computer Science, vol. 70, 13-20, 2006.

The four outcomes of a classifier. Available from:

R. Kohavi, “A study of cross-validation and bootstrap for accuracy estimation and model selection,” In International Joint Conference on Artificial Intelligence, Montreal, Canada, 1995, pp. 1137-1145.

L.J. Sheela, “A Review of Sentiment Analysis in Twitter Data Using Hadoop,” International Journal of Database Theory and Application, vol. 9, no. 1, pp. 77-86, 2016.

How to Cite
O. Coban, B. Ozyildirim, and S. Ozel, “An Empirical Study of the Extreme Learning Machine for Twitter Sentiment Analysis”, IJISAE, vol. 6, no. 3, pp. 178-184, Sep. 2018.
Research Article