Using Hashtags to Capture Fine Emotion Categories from Tweets

Detecting emotions in microblogs and social media posts has applications for industry, health, and security. Statistical, supervised automatic methods for emotion detection rely on text that is labeled for emotions, but such data is rare and available for only a handful of basic emotions. In this paper, we show that emotion-word hashtags are good manual labels of emotions in tweets. We also propose a method to generate a large lexicon of word–emotion associations from this emotionlabeled tweet corpus. This is the first lexicon with real-valued word–emotion association scores. We begin with experiments for six basic emotions and show that the hashtag annotations are consistent and match with the annotations of trained judges. We also show how the extracted tweets corpus and word–emotion associations can be used to improve emotion classification accuracy in a different non-tweets domain. Eminent psychologist, Robert Plutchik, had proposed that emotions have a relationship with personality traits. However, empirical experiments to establish this relationship have been stymied by the lack of comprehensive emotion resources. Since personality may be associated with any of the hundreds of emotions, and since our hashtag approach scales easily to a large number of emotions, we extend our corpus by collecting tweets with hashtags pertaining to 585 fine emotions. Then, for the first time, we present experiments to show that fine emotion categories such as that of excitement, guilt, yearning, and admiration are useful in automatically detecting personality from text. Stream-of-consciousness essays and collections of Facebook posts marked with personality traits of the author are used as the test sets.