Howard R. Turtle

Also published as: Howard Turtle


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Exploring Fine-Grained Emotion Detection in Tweets
Jasy Suet Yan Liew | Howard R. Turtle
Proceedings of the NAACL Student Research Workshop

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EmoTweet-28: A Fine-Grained Emotion Corpus for Sentiment Analysis
Jasy Suet Yan Liew | Howard R. Turtle | Elizabeth D. Liddy
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

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.


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Commercial Impact of VLC Research
Howard Turtle
Fifth Workshop on Very Large Corpora