Summary of the paper

Title EmoTweet-28: A Fine-Grained Emotion Corpus for Sentiment Analysis
Authors Jasy Suet Yan Liew, Howard R. Turtle and Elizabeth D. Liddy
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.
Topics Corpus (Creation, Annotation, etc.), Emotion Recognition/Generation, Opinion Mining / Sentiment Analysis
Full paper EmoTweet-28: A Fine-Grained Emotion Corpus for Sentiment Analysis
Bibtex @InProceedings{LIEW16.189,
  author = {Jasy Suet Yan Liew and Howard R. Turtle and Elizabeth D. Liddy},
  title = {EmoTweet-28: A Fine-Grained Emotion Corpus for Sentiment Analysis},
  booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
  year = {2016},
  month = {may},
  date = {23-28},
  location = {Portoro┼ż, Slovenia},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Sara Goggi and Marko Grobelnik and Bente Maegaard and Joseph Mariani and Helene Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  address = {Paris, France},
  isbn = {978-2-9517408-9-1},
  language = {english}
 }
Powered by ELDA © 2016 ELDA/ELRA