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A multidimensional approach for detecting irony in Twitter (Information…
A multidimensional approach for detecting irony in Twitter
Meta
Year
2012
Author
Paolo Rosso
Tony Veale
Antonio Reyes
Goals
Objective
Irony detection
Information
We construct a new model of irony detection that is assessed along two dimensions: representativeness and relevance
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 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’’.
Irony. This linguistic phenomenon, which is widespread in web content, has important implications for tasks such as sentiment analysis
Problem of irony cuts through every aspect of language, from pronunciation to lexical choice, syntactic structure, semantics and conceptualization. As such, it is unrealistic to seek a computational silver bullet for irony, and a general solution will not be found in any single technique or algorithm.
(cf. Reyes et al. 2009 about the importance of determining the presence of irony in order to assign fine-grained polarity levels),
Opinion mining (cf. Sarmento et al. 2009), where the authors note the role of irony in discriminating negative from positive opinions), and advertising (cf. Kreuz 2001, about the function of irony to increase message effectiveness in advertising), among others.
Rather, we must try to identify specific aspects and forms of irony that are susceptible to computational analysis, and from these individual treatments attempt to synthesize a gradually broader solution.
The impact of this work thus lies in the way it deals with non-factual information that is linguistically expressed, such as sentiment, attitude, humor and mood. These are inherent to our social activities, and are therefore extremely useful in the automatic mining of new knowledge.
In this current work, we aim to analyze irony in terms of a multidimensional model of textual elements. We thus identify a set of discriminative features to automatically differentiate an ironic text from a non-ironic one.
Irony in language, theoratical problem
Pretense
Sarcasm and irony