Title: SVM-based sentiment classification: a comparative study against state-of-the-art classifiers

Authors: Dionisios N. Sotiropoulos; Demitrios E. Pournarakis; George M. Giaglis

Addresses: Dept. of Management Science & Technology, Athens University of Economics and Business, Evelpidon 47a & Lefkados St, Greece ' Dept. of Management Science & Technology, Athens University of Economics and Business, Evelpidon 47a & Lefkados St, Greece ' Dept. of Management Science & Technology, Athens University of Economics and Business, Evelpidon 47a & Lefkados St, Greece

Abstract: Transforming the unstructured textual information contained in various social media streams into useful business knowledge is an extremely difficult computational task, mainly, due to the underlying hard pattern classification problem of sentiment analysis, especially within the context of the Greek language. In this paper, we address the pattern classification problem of sentiment analysis through the utilisation of support vector machines (SVMs). In particular, we conducted an extensive experimental comparison where we tested the aforementioned classifier against a set of state-of-the-art machine learning classifiers on a benchmark dataset originating from the Greek bank sector by collecting data from the streaming API of Twitter that were explicitly referring to the major banks of Greece. Our results present classification accuracy and execution time metrics for each classifier, revealing the superiority of the SVM learning paradigm in assigning patterns to the correct sentiment class.

Keywords: sentiment analysis; support vector machines; SVMs; classification; Twitter.

DOI: 10.1504/IJCISTUDIES.2017.086063

International Journal of Computational Intelligence Studies, 2017 Vol.6 No.1, pp.52 - 67

Received: 31 Oct 2016
Accepted: 31 Oct 2016

Published online: 11 Aug 2017 *

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