Irony: Negation, echo and metarepresentation

2013

Knowl Inf Syst

109

Rosso, P. (2014Abstract. It is well-known that irony is one of the most subtle devices used to, in a refined way and without a negation marker, deny what is literally said. As such, its automatic detection would represent valuable knowledge regarding tasks as diverse as sentiment analysis, information extraction, or decision making. The research described in this article is focused on identifying key values of components to represent underlying characteristics of this linguistic phenomenon. In absence of a negation marker, we focus on representing the core of irony by means of three conceptual layers. These layers involve 8 different textual features. By representing four available data sets with these features, we try to find hints about how to deal with this unexplored task from a computational point of view. Our findings are assessed by human annotators in two strata: isolated sentences and entire documents. The results show how complex and subjective the task of automatically detecting irony could be.

Section: Ironymentioning

confidence: 93%

“…Its δ, thus, is 0.66. The following phase was focused on obtaining the documents with greater probability to have ironic content 11 . These documents were obtained by applying Formula 2:…”

Section: Experimental Set-upmentioning

confidence: 99%

On the difficulty of automatically detecting irony: beyond a simple case of negation

Reyes¹,

2013

Knowl Inf Syst

109

“…Concerning verbal irony, literature suggests a prototypical characteristic, it intentionally denies what is literally expressed [13], i.e. an indirect negation [17].…”

Section: Ironymentioning

confidence: 99%

From humor recognition to irony detection: The figurative language of social media

Reyes

Data & Knowledge Engineering

Buscaldi

2012

321

173

ElsevierReyes Pérez, A.; Rosso, P.; Buscaldi, D. (2012) AbstractThe research described in this paper focuses on analyzing two playful domains of language: humor and irony, in order to identify key values components for their automatic processing. In particular, we focus on describing a model for recognizing these phenomena in social media, such as "tweets". Our experiments are centered on five data sets retrieved from Twitter taking advantage of usergenerated tags, such as "#humor" and "#irony". The model, which is based on textual features, is assessed on two dimensions: representativeness and relevance. The results, apart from providing some valuable insights into the creative and figurative usages of language, are positive regarding humor, and encouraging regarding irony.

“…Our work focuses on verbal irony. This kind of irony is defined as a way of intentionally denying what it is literally expressed [11]; i.e. a kind of indirect negation [15].…”

Section: Pragmatic Theories Of Ironymentioning

confidence: 99%

“…If the accuracy achieved is similar in all the experiments (cf. 11 In this case we focused on keeping a relation 1 to 3 because the figurative contents (either ironic, satiric, or sarcastic) do not appear in real contexts in a relation 1 to 1. 12 A total of 17 features (n-grams from 2 to 7; POS-grams from 2 to 7; funny, negative, positive, affective, and pleasantness profiling).…”

Section: Re-evaluating the Modelmentioning

confidence: 99%

Making objective decisions from subjective data: Detecting irony in customer reviews

Reyes

Decision Support Systems

2012

142

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 as 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 on 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.