A CCG-based Approach to Fine-Grained Sentiment Analysis
Phillip Smith, Mark Lee · 2012
In this paper, we present a Combinatory Categorial Grammar (CCG) based approach to the classification of emotion in short texts. We develop a method that makes use of the notion put forward by Ortony et al. (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), and by taking the other competing systems as a baseline, demonstrate that our approach to emotion categorisation performs favourably.