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 are rare and available for only a handful of basic emotions. In this article, 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 wordemotion associations from this emotion-labeled tweet corpus. This is the first lexicon with real-valued wordemotion 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 tweet corpus and wordemotion associations can be used to improve emotion classification accuracy in a different nontweet domain.
hashtags tweets social media basic emotions wordemotion associations affect personality detection big 5 model sentiment analysis