From humor recognition to irony detection: The figurative language of social media
By Antonio Reyes, Paolo Rosso, Davide Buscaldi. Data & Knowledge Engineering, 2012
401 citations recorded by the source corpus.
The research described in this paper is focused on analyzing two playful domains of language: humor and irony, in order to identify key values components for their automatic processing. In particular, we are focused 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 user-generated 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.
Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Social Psychology
View Paper: https://www.semanticscholar.org/paper/0b447bc717789cf79b8de76b485a763d21c9ed21
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In Artificial Intelligence
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- Adam Poliak
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In Experimental and Cognitive Psychology
In Social Psychology
Citations in the corpus
- Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation (2018, background): “Understanding and recognizing figurative language “entail[s] cognitive capabilities to abstract and meta-represent meanings beyond physical words” (Reyes et al., 2012).”
- Towards a Unified Natural Language Inference Framework to Evaluate Sentence Representations (2018, background): “Understanding and recognizing figurative language “entail[s] cognitive capabilities to abstract and meta-represent meanings beyond physical words” (Reyes et al., 2012).”
- Sarcasm Detection on Twitter: A Behavioral Modeling Approach (2015, background): “[28] focused on developing classifiers to detect verbal irony based on ambiguity, polarity, unexpectedness and emotional cues derived from text.”
- Humour 2.0: Styles and Types of Humour and Virality of Memes on Facebook (2015, background): “It has been defined as ‘the presence of amusing effects, such as laughter or well-being sensations’ (Reyes et al., 2012).”
- Sentiment analysis: A review and comparative analysis of web services (2015, background): “) which are essential to detect literary devices such as irony or sarcasm [74,75], and consequently, to find and rate sentiments.”