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

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.