Lexical Influences on the Perception of Sarcasm

By Roger Kreuz, Gina M. Caucci. 2007

189 citations recorded by the source corpus.

Speakers and listeners make use of a variety of pragmatic factors to produce and identify sarcastic statements. It is also possible that lexical factors play a role, although this possibility has not been investigated previously. College students were asked to read excerpts from published works that originally contained the phrase said sarcastically, although the word sarcastically was deleted. The participants rated the characters' statements in these excerpts as more likely to be sarcastic than those from similar excerpts that did not originally contain the word sarcastically. The use of interjections, such as gee or gosh, predicted a significant amount of the variance in the participants' ratings of sarcastic intent. This outcome suggests that sarcastic statements may be more formulaic than previously realized. It also suggests that computer software could be written to recognize such lexical factors, greatly increasing the likelihood that non-literal intent could be correctly interpreted by such programs, even if they are unable to identify the pragmatic components of nonliteral language.

Subfields: Experimental and Cognitive Psychology, Philosophy

View Paper: https://www.semanticscholar.org/paper/89fa65816c7cad65e9b00652f0be130bea97ec46

How researchers see this paper

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

In Experimental and Cognitive Psychology

In Philosophy

Citations in the corpus

Sarcasm Detection on Twitter: A Behavioral Modeling Approach (2015, background): “While studies on automatic detection of sarcasm in speech [35] utilizes prosodic, spectral and contextual features, sarcasm detection in text has relied on identifying text patterns [4] and lexical features [10, 17].”
Fracking Sarcasm using Neural Network (2016, background): “Kreuz and Caucci (Kreuz and Caucci, 2007) explored the role of lexical indicators, such as interjections (e.”
Identifying Sarcasm in Twitter: A Closer Look (2011, methodology): “Our approach of looking at lexical features for identification of sarcasm was inspired by the work of Kreuz and Caucci (2007).”
Contextualized Sarcasm Detection on Twitter (2015, background): “Most approaches to sarcasm detection to date have treated the task primarily as a text categorization problem, relying on the insights of Kreuz and Caucci (2007) that sarcastic utterances often contain lexical indicators (such as interjections and intensifiers) and other linguistic markers (such as…”
Verbal Irony (2012, background): “Kreuz and Caucci (2007) Diminutives “Dat was een goed ideetje.”
Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science (2018, background): “Here, our focus is on linguistic data. the review (e.g., Pang et al., 2002) or the hashtag #sarcasm used to identify sarcastic language (e.g., Kreuz and Caucci, 2007).”