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

Authors

  • Onder Coban Cukurova University
  • Buse Melis Ozyildirim Cukurova University
  • Selma Ayse Ozel Cukurova University

DOI:

https://doi.org/10.18201/ijisae.2018644774

Keywords:

Twitter, Sentiment Analysis, Support Vector Machine, Extreme Learning Machine

Abstract

Extreme Learning Machine (ELM) method is proposed for single hidden layer feed-forward networks (SLFNs). The ELM
employs feed-forward neural network architecture and works with randomly determined input weights. In this aspect, ELM depends on
principle 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 Support
Vector Machines (SVM) and Deep Neural Networks. After the feature mapping, the main goal of the ELM is to learn weights between
hidden and output layers by minimizing the error. The ELM has gained much more popularity recently; and can be utilized for
classification, regression, and dimension reduction. In literature, Twitter sentiment analysis is generally considered as a classification
task. Therefore, in this study, the basic ELM is utilized for Twitter sentiment analysis and compared with the SVM which is one of the
most 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

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Published

26.09.2018

How to Cite

Coban, O., Ozyildirim, B. M., & Ozel, S. A. (2018). An Empirical Study of the Extreme Learning Machine for Twitter Sentiment Analysis. International Journal of Intelligent Systems and Applications in Engineering, 6(3), 178–184. https://doi.org/10.18201/ijisae.2018644774

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Section

Research Article