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
A survey of trust and reputation systems for online service provision
Decision Support Systems
Multi-facet Rating of Product Reviews
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
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
Revising the wordnet domains hierarchy: semantics, coverage and balancing
MLR '04 Proceedings of the Workshop on Multilingual Linguistic Ressources
NeSp-NLP '10 Proceedings of the Workshop on Negation and Speculation in Natural Language Processing
Semi-supervised recognition of sarcastic sentences in Twitter and Amazon
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Statistical substring reduction in linear time
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
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
More than words: Social networks' text mining for consumer brand sentiments
Expert Systems with Applications: An International Journal
Potential Power and Problems in Sentiment Mining of Social Media
International Journal of Strategic Decision Sciences
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The research described in this work focuses on identifying key components for the task of irony detection. By means of analyzing a set of customer reviews, which are considered ironic both in social and mass media, we try to find hints about how to deal with this task from a computational point of view. Our objective is to gather a set of discriminating elements to represent irony, in particular, the kind of irony expressed in such reviews. To this end, we built a freely available data set with ironic reviews collected from Amazon. Such reviews were posted on the basis of an online viral effect; i.e. contents that trigger a chain reaction in people. The findings were assessed employing three classifiers. Initial results are largely positive, and provide valuable insights into the subjective issues of language facing tasks such as sentiment analysis, opinion mining and decision making.