A CCG-based Approach to Fine-Grained Sentiment Analysis in Microtext

Phillip Smith, M. Lee

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Citations (Scopus)

Abstract

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 publicationPROCEEDINGS OF THE TWENTY-SEVENTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE
Place of PublicationUS
PublisherAAAI
Pages80-86
ISBN (Print)978-1-57735-615-8
Publication statusPublished - 2013
EventAAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE - Washington, United States
Duration: 14 Jul 201318 Jul 2013

Conference

ConferenceAAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE
CountryUnited States
Period14/07/1318/07/13

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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.