Leveraging Social Media Linguistic Features for Bilingual Microblog Sentiment Classification

Abstract

Social media and microblogs have become an integral part of everyday life. People use microblogs to communicate with each other, express their opinion about a wide range of topics and inform themselves about issues they are interested in. The increasing volume of information generated in microblogs has led to the need of automatically determining the sentiment expressed in microblog comments. Researchers have worked in systematically analyzing microblog comments in order to identify the sentiment expressed in them. Most work in sentiment analysis of microblog comments has been focused on comments written in the English language, whereas fewer efforts have been made in predicting the sentiment of Greek microblog comments. In this paper, we propose a lexicon-based sentiment analysis algorithm for the sentiment classification of both Greek and English microblog comments. The proposed method uses a unified approach for determining the sentiment of comments written in both languages and incorporates techniques that exploit the distinctive features of the language used in microblogs in order to accurately predict the sentiment expressed in microblog comments. Our approach achieves promising results for the sentiment classification of microblog comments into positive, negative or neutral.