In this paper, we present a Combinatory Categorial Grammar (CCG) based approach to the classification of emotion in microtext. We develop a method that makes use of the notion put forward by Ortony, Clore, and Collins (1988), that emotions are valenced reactions. This hypothesis sits central to our system, in which we adapt contextual valence shifters to infer the emotional content of a text. We integrate this with an augmented version of WordNet-Affect, which acts as our lexicon. Finally, we experiment with a corpus of headlines proposed in the 2007 SemEval Affective Task (Strapparava and Mihalcea 2007) as our microtext corpus, and by taking the other competing systems as a baseline, demonstrate that our approach to emotion categorisation performs favourably.
|Title of host publication||PROCEEDINGS OF THE TWENTY-SEVENTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE|
|Place of Publication||US|
|Publication status||Published - 2013|
|Event||AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE - Washington, United States|
Duration: 14 Jul 2013 → 18 Jul 2013
|Conference||AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE|
|Period||14/07/13 → 18/07/13|
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Smith, P., & Lee, M. (2013). A CCG-based Approach to Fine-Grained Sentiment Analysis in Microtext. In PROCEEDINGS OF THE TWENTY-SEVENTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE (pp. 80-86). US: AAAI.