A unified theory of irony and its computational formalization
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Computational Linguistics
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Making computers laugh: investigations in automatic humor recognition
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Characterizing Humour: An Exploration of Features in Humorous Texts
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
Automatic satire detection: are you having a laugh?
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Enhanced sentiment learning using Twitter hashtags and smileys
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Identifying sarcasm in Twitter: a closer look
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
How can you say such things?!?: recognizing disagreement in informal political argument
LSM '11 Proceedings of the Workshop on Languages in Social Media
Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach
Proceedings of the 20th ACM international conference on Information and knowledge management
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
What's in a hashtag?: content based prediction of the spread of ideas in microblogging communities
Proceedings of the fifth ACM international conference on Web search and data mining
We know what @you #tag: does the dual role affect hashtag adoption?
Proceedings of the 21st international conference on World Wide Web
Making objective decisions from subjective data: Detecting irony in customer reviews
Decision Support Systems
Automatic humor classification on Twitter
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop
Twitter n-gram corpus with demographic metadata
Language Resources and Evaluation
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Sarcasm is a form of speech act in which the speakers convey their message in an implicit way. The inherently ambiguous nature of sarcasm sometimes makes it hard even for humans to decide whether an utterance is sarcastic or not. Recognition of sarcasm can benefit many sentiment analysis NLP applications, such as review summarization, dialogue systems and review ranking systems. In this paper we experiment with semi-supervised sarcasm identification on two very different data sets: a collection of 5.9 million tweets collected from Twitter, and a collection of 66000 product reviews from Amazon. Using the Mechanical Turk we created a gold standard sample in which each sentence was tagged by 3 annotators, obtaining F-scores of 0.78 on the product reviews dataset and 0.83 on the Twitter dataset. We discuss the differences between the datasets and how the algorithm uses them (e.g., for the Amazon dataset the algorithm makes use of structured information). We also discuss the utility of Twitter #sarcasm hashtags for the task.