Emoticons and Online Message Interpretation
Social Science Computer Review
Lexical influences on the perception of sarcasm
FigLanguages '07 Proceedings of the Workshop on Computational Approaches to Figurative Language
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
Semi-supervised recognition of sarcastic sentences in Twitter and Amazon
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
From humor recognition to irony detection: The figurative language of social media
Data & Knowledge Engineering
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
Extracting fine-grained durations for verbs from Twitter
ACL '12 Proceedings of ACL 2012 Student Research Workshop
A system for real-time Twitter sentiment analysis of 2012 U.S. presidential election cycle
ACL '12 Proceedings of the ACL 2012 System Demonstrations
Automatically constructing a normalisation dictionary for microblogs
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
TwiSent: a multistage system for analyzing sentiment in twitter
Proceedings of the 21st ACM international conference on Information and knowledge management
Lexical normalization for social media text
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
Distant supervision for emotion classification with discrete binary values
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
Some clues on irony detection in tweets
Proceedings of the 22nd international conference on World Wide Web companion
Using explicit linguistic expressions of preference in social media to predict voting behavior
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Twitter n-gram corpus with demographic metadata
Language Resources and Evaluation
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Sarcasm transforms the polarity of an apparently positive or negative utterance into its opposite. We report on a method for constructing a corpus of sarcastic Twitter messages in which determination of the sarcasm of each message has been made by its author. We use this reliable corpus to compare sarcastic utterances in Twitter to utterances that express positive or negative attitudes without sarcasm. We investigate the impact of lexical and pragmatic factors on machine learning effectiveness for identifying sarcastic utterances and we compare the performance of machine learning techniques and human judges on this task. Perhaps unsurprisingly, neither the human judges nor the machine learning techniques perform very well.