A novel hybrid approach for sentiment classification of Turkish tweets for GSM operators

Abstract

The increase in the amount of content shared on social media makes it difficult to extract meaningful information from scientific studies. Accordingly, in recent years, researchers have been working extensively on sentiment analysis studies for the automatic evaluation of social media data. One of the focuses of these studies is sentiment analysis on tweets. The more tweets are available, the more features in terms of words exist. This leads to the curse of dimensionality and sparsity, resulting in a decrease in the success of the classification. In this study, Gini Index, Information Gain and Genetic Algorithm (GA) are used for feature selection and Support Vector Machines (SVMs), Artificial Neural Networks (ANN) and Centroid Based classification algorithms are used for the classification of Turkish tweets obtained from 3 different GSM operators. The feature selection methods are combined with the classification methods to investigate the effect on the success rate of analysis. Especially, when the SVMs are used with the GA as a hybrid, 96.8% success has been achieved for the classification of the tweets as positive or negative. © 2018, North Atlantic University Union. All rights reserved.