GreekPolitics: Sentiment Analysis on Greek Politically Charged Tweets

The rapid growth of on-line social media platforms has rendered opinion mining/sentiment analysis a critical area of Natural Language Processing (NLP) research. This paper focuses on analyzing Twitter posts (tweets), written in the Greek language and politically charged in content. This is a rather underexplored topic, due to the scarcity of publicly available annotated datasets. Thus, we present and release “GreekPolitics”, i.e., a dataset of Greek tweets with politically charged content, independently annotated across four different sentiment dimensions: polarity, figurativeness, aggressiveness and bias. GreekPolitics has been evaluated comprehensively in a classification setting, separately for each sentiment, using state-of-the-art Deep Neural Networks (DNNs) and data augmentation methods. This paper details the dataset, the evaluation process and the experimental results. Based on these, best practices are identified for achieving the highest classification accuracy at the test stage.