@inproceedings{L16-1183,
 abstract = {This paper describes EmoTweet-28, a carefully curated corpus of 15,553 tweets annotated with 28 emotion categories for the purpose of training and evaluating machine learning models for emotion classification. EmoTweet-28 is, to date, the largest tweet corpus annotated with fine-grained emotion categories. The corpus contains annotations for four facets of emotion: valence, arousal, emotion category and emotion cues. We first used small-scale content analysis to inductively identify a set of emotion categories that characterize the emotions expressed in microblog text. We then expanded the size of the corpus using crowdsourcing. The corpus encompasses a variety of examples including explicit and implicit expressions of emotions as well as tweets containing multiple emotions. EmoTweet-28 represents an important resource to advance the development and evaluation of more emotion-sensitive systems.
},
 address = {Portorož, Slovenia},
 author = {Jasy Suet Yan Liew and Howard R. Turtle and Elizabeth D. Liddy},
 booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
 month = {May},
 pages = {1149--1156},
 publisher = {European Language Resources Association (ELRA)},
 title = {EmoTweet-28: A Fine-Grained Emotion Corpus for Sentiment Analysis},
 url = {https://www.aclweb.org/anthology/L16-1183},
 year = {2016}
}