Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Growing finely-discriminating taxonomies from seeds of varying quality and size
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Creative language retrieval: a robust hybrid of information retrieval and linguistic creativity
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
From humor recognition to irony detection: The figurative language of social media
Data & Knowledge Engineering
A context-sensitive, multi-faceted model of lexico-conceptual affect
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
A multidimensional approach for detecting irony in Twitter
Language Resources and Evaluation
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Irony is an effective but challenging mode of communication that allows a speaker to express sentiment-rich viewpoints with concision, sharpness and humour. Irony is especially common in online documents that express subjective and deeply-felt opinions, and thus represents a significant obstacle to the accurate analysis of sentiment in web texts. In this paper we look at one commonly used framing device for linguistic irony --the simile --to show how irony is often marked in ways that make it computationally feasible to detect. We conduct a very large corpus analysis of web-harvested similes to identify the most interesting characteristics of ironic comparisons, and provide an empirical evaluation of a new algorithm for separating ironic from non-ironic similes.