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Social media analysis during political turbulence
#MMPMID29088263
Antonakaki D
; Spiliotopoulos D
; V Samaras C
; Pratikakis P
; Ioannidis S
; Fragopoulou P
PLoS One
2017[]; 12
(10
): e0186836
PMID29088263
show ga
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.
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