A CCG-based approach to fine-grained sentiment analysis in microtext

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationAnalyzing Microtext - Papers from the AAAI Spring Symposium, Technical Report
Number of pages7
Publication statusPublished - 5 Sept 2013
Event2013 AAAI Spring Symposium - Palo Alto, CA, United States
Duration: 25 Mar 201327 Mar 2013


Conference2013 AAAI Spring Symposium
Country/TerritoryUnited States
CityPalo Alto, CA

ASJC Scopus subject areas

  • Artificial Intelligence


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