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 language | English |
|---|---|
| Title of host publication | PROCEEDINGS OF THE TWENTY-SEVENTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE |
| Place of Publication | US |
| Publisher | AAAI |
| Pages | 80-86 |
| ISBN (Print) | 978-1-57735-615-8 |
| Publication status | Published - 2013 |
| Event | AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE - Washington, United States Duration: 14 Jul 2013 → 18 Jul 2013 |
Conference
| Conference | AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE |
|---|---|
| Country | United States |
| Period | 14/07/13 → 18/07/13 |
Bibliographical note
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Cite this
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.