A multidimensional approach for detecting irony in Twitter

By Antonio Reyes, Paolo Rosso, Tony Veale. Language Resources and Evaluation, 2013

464 citations recorded by the source corpus.

Irony is a pervasive aspect of many online texts, one made all the more difficult by the absence of face-to-face contact and vocal intonation. As our media increasingly become more social, the problem of irony detection will become even more pressing. 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”. 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 construct a new model of irony detection that is assessed along two dimensions: representativeness and relevance. Initial results are largely positive, and provide valuable insights into the figurative issues facing tasks such as sentiment analysis, assessment of online reputations, or decision making.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/40a60a42a5da2461cb3da495992785b288a63d1f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

Humans Require Context to Infer Ironic Intent (so Computers Probably do, too) (2014, background): “…source used to experiment with irony detection systems has been Twitter (Reyes et al., 2012; Gonz´alez-Ib´a˜nez et al., 2011; Davidov et al., 2010), though Amazon product reviews have been used experimentally as well (Tsur et al., 2010; Davidov et al., 2010; Reyes et al., 2012; Filatova, 2012).”
A Systematic Review of Emoji: Current Research and Future Perspectives (2019, background): “Some research has focused on the ironic features of emoji and developed an irony detection model for emoji in order to improve the accuracy of sentiment analysis of tweets (Reyes et al., 2013; Prasad et al., 2017; Singh et al., 2019).”
Contextualized Sarcasm Detection on Twitter (2015, background): “…be surprisingly accurate across different domains (Carvalho et al. 2009; Davidov, Tsur, and Rappoport 2010; González-Ibáñez, Muresan, and Wacholder 2011; Riloff et al. 2013; Lukin and Walker 2013; Reyes, Rosso, and Veale 2013), but are divorced from any notion of their potentially useful context.”
SemEval-2018 Task 3: Irony Detection in English Tweets (2018, background): “…include punctuation mark/interjection counts (e.g Davi-dov et al., 2010), sentiment lexicon scores (e.g. Bouazizi and Ohtsuki, 2016; Far´ıas et al., 2016), emoji (e.g. Gonz´alez-Ib´a˜nez et al., 2011), writing style, emotional scenarios, part of speech-patterns (e.g. Reyes et al., 2013), and so on.”
Modelling input texts: from Tree Kernels to Deep Learning (2015, background): “objective parts of a document [107, 187], opinion holders [70] or more complex sentiments and emotions [22], in particular, as irony or sarcasm [26, 123].”
Like It or Not (2016, background): “…the interest of many researchers who analyzed Twitter data for a variety of different tasks such as making predictions [Bollen et al. 2011], detecting users’ sentiment towards different topics [Go et al. 2009], detecting users’ emotions [Mohammad 2012], and detecting irony [Reyes et al. 2013].”
Challenges in Sentiment Analysis (2017, background): “There is growing interest in detecting figurative language, especially irony and sarcasm (Carvalho et al. 2009; Reyes et al. 2013; Veale and Hao 2010; Filatova 2012; González-Ibánez et al. 2011).”
A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks (2016, background): “(Reyes et al., 2013) described a set of textual features for recognizing irony at a linguistic level, especially in short texts created via Twitter, and constructed a new model that was assessed along two dimensions: representativeness and relevance.”