WordNet: a lexical database for English
Communications of the ACM
A unified theory of irony and its computational formalization
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Technologies That Make You Smile: Adding Humor to Text-Based Applications
IEEE Intelligent Systems
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Inter-coder agreement for computational linguistics
Computational Linguistics
WordNet: similarity - measuring the relatedness of concepts
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Why are they excited?: identifying and explaining spikes in blog mood levels
EACL '06 Proceedings of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics: Posters & Demonstrations
Automatic creation of a reference corpus for political opinion mining in user-generated content
Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion
Clues for detecting irony in user-generated contents: oh...!! it's "so easy" ;-)
Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion
Automatic satire detection: are you having a laugh?
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Generating high-coverage semantic orientation lexicons from overtly marked words and a thesaurus
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Detecting Ironic Intent in Creative Comparisons
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Mining subjective knowledge from customer reviews: a specific case of irony detection
WASSA '11 Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis
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Irony is a pervasive aspect of many online texts, one made all the more difficult by the absence of face-to-face contact and vocal intonation. As our media increasingly become more social, the problem of irony detection will become even more pressing. We describe here a set of textual features for recognizing irony at a linguistic level, especially in short texts created via social media such as Twitter postings or "tweets". Our experiments concern four freely available data sets that were retrieved from Twitter using content words (e.g. "Toyota") and user-generated tags (e.g. "#irony"). We construct a new model of irony detection that is assessed along two dimensions: representativeness and relevance. Initial results are largely positive, and provide valuable insights into the figurative issues facing tasks such as sentiment analysis, assessment of online reputations, or decision making.