Paper: Learning Emotion Indicators from Tweets: Hashtags, Hashtag Patterns, and Phrases

ACL ID D14-1127
Title Learning Emotion Indicators from Tweets: Hashtags, Hashtag Patterns, and Phrases
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

We present a weakly supervised approach for learning hashtags, hashtag patterns, and phrases associated with five emotions: AFFEC- TION, ANGER/RAGE, FEAR/ANXIETY, JOY, and SADNESS/DISAPPOINTMENT. Starting with seed hashtags to label an initial set of tweets, we train emotion classifiers and use them to learn new emotion hashtags and hash- tag patterns. This process then repeats in a bootstrapping framework. Emotion phrases are also extracted from the learned hashtags and used to create phrase-based emotion clas- sifiers. We show that the learned set of emo- tion indicators yields a substantial improve- ment in F-scores, ranging from +%5 to +%18 over baseline classifiers.