Assessment of Iterative Semi-Supervised Feature Selection Learning for Sentiment Analyses: Digital Currency Markets

Abstract

Currently, the cryptocurrencies have been highly attracted by investors. Due to the lack of a central money authority, the stock markets are experiencing large price fluctuations based on speculation. In this study, our goal is to try to determine the effects of social media topics in digital money markets by using sentiment analysis methods on natural data. First, related comments on Twitter about digital currencies were collected by using a web crawler. The comments investigated with feature selection metrics, semi-supervised feature selection metrics, Random Forest, Support Vector Machine (SVM) methods. The Random Forest was observed to perform the most successful and fastest sentiment classification process using a combination of semi-supervised feature selection metrics by iterative actions. Iterative Semi-Supervised Feature Selection (ISSFS) method proposed and evaluated in this paper. As a result of the experiments, the optimum number of words for the English language was calculated in order to perform sentiment analysis of digital currency markets. Classifiers and methods used were shared as results.