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Abstract
Today, a considerable proportion of the public political discourse on nationwide elections proceeds in Online Social Networks. Through analyzing this content, we can discover the major themes that prevailed during the discussion, investigate the temporal variation of positive and negative sentiment and examine the semantic proximity of these themes. According to existing studies, the results of similar tasks are heavily dependent on the quality and completeness of dictionaries for linguistic preprocessing, entity discovery and sentiment analysis. Additionally, noise reduction is achieved with methods for sarcasm detection and correction. Here we report on the application of these methods on the complete corpus of tweets regarding two local electoral events of worldwide impact: the Greek referendum of 2015 and the subsequent legislative elections. To this end, we compiled novel dictionaries for sentiment and entity detection for the Greek language tailored to these events. We subsequently performed volume analysis, sentiment analysis, sarcasm correction and topic modeling. Results showed that there was a strong anti-austerity sentiment accompanied with a critical view on European and Greek political actions.
Highlights
It is common ground that Online Social Networks (OSNs) have prevailed as the major platform of public expression regarding political matters
Opinion polls that were conducted during the same period showed an opposite trend, which, according to post-referendum analysis, was erratic [45]
There was a slight increase in the volume referring to the SYRIZA’s major opposition party, New Democracy (ND)
Summary
It is common ground that Online Social Networks (OSNs) have prevailed as the major platform of public expression regarding political matters. Existing studies have performed elaborate analyses in order to investigate the behavior of online users during pre-election periods. The purpose of most of these studies was to generate patterns that distinguish users’ or posts’ favoritism towards one political party or certain ideology. The main predicament in these studies was to generate election predictions that are close or even outperform public opinion polls [1], to measure approval ratings [2] or to assess public opinion during political debates [3]. There exist studies that have tried to measure the emotional content in social media [4]. One of the first studies that compared sentiment analysis in Twitter with “traditional” opinion polls was from 2010, demonstrating a strong correlation
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