Emotional tweets

Abstract

Detecting emotions in microblogs and social media posts has applications for industry, health, and security. However, there exists no microblog corpus with instances labeled for emotions for developing supervised systems. In this paper, we describe how we created such a corpus from Twitter posts using emotionword hashtags. We conduct experiments to show that the self-labeled hashtag annotations are consistent and match with the annotations of trained judges. We also show how the Twitter emotion corpus can be used to improve emotion classification accuracy in a different domain. Finally, we extract a word-emotion association lexicon from this Twitter corpus, and show that it leads to significantly better results than the manually craftedWordNet Affect lexicon in an emotion classification task.