Sciweavers

Share
ACL
2015

Sparse, Contextually Informed Models for Irony Detection: Exploiting User Communities, Entities and Sentiment

3 years 2 months ago
Sparse, Contextually Informed Models for Irony Detection: Exploiting User Communities, Entities and Sentiment
Automatically detecting verbal irony (roughly, sarcasm) in online content is important for many practical applications (e.g., sentiment detection), but it is difficult. Previous approaches have relied predominantly on signal gleaned from word counts and grammatical cues. But such approaches fail to exploit the context in which comments are embedded. We thus propose a novel strategy for verbal irony classification that exploits contextual features, specifically by combining noun phrases and sentiment extracted from comments with the forum type (e.g., conservative or liberal) to which they were posted. We show that this approach improves verbal irony classification performance. Furthermore, because this method generates a very large feature space (and we expect predictive contextual features to be strong but few), we propose a mixed regularization strategy that places a sparsity-inducing 1 penalty on the contextual feature weights on top of the 2 penalty applied to all model coeffi...
Byron C. Wallace, Do Kook Choe, Eugene Charniak
Added 13 Apr 2016
Updated 13 Apr 2016
Type Journal
Year 2015
Where ACL
Authors Byron C. Wallace, Do Kook Choe, Eugene Charniak
Comments (0)
books