Emoji as emotion tags for tweets

In many natural language processing tasks, supervised machine learning approaches have proved most effective, and substantial effort has been made into collecting and annotating corpora for building such models. Emotion detection from text is no exception; however, research in this area is in its relative infancy, and few emotion annotated corpora exist to date. A further issue regarding the development of emotion annotated corpora is the difficulty of the annotation task and resulting inconsistencies in human annotations. One approach to address these problems is to use self-annotated data, using explicit indications of emotions included by the author of the data in question. We present a study of the use of unicode emoji as self-annotation of a Twitter user’s emotional state. Emoji are found to be used far more extensively than hash tags and we argue that they present a more faithful representation of a user’s emotional state. A substantial set of tweets containing emotion indicative emoji are collected and a sample annotated for emotions. The accuracy and utility of emoji as emotion labels are evaluated directly (with respect to annotations) and through trained statistical models. Results are cautiously optimisticand suggest further study of emotji usage.