Sarcastic Soulmates: Intimacy and irony markers in social media messaging

Koen Hallmann, Florian Kunneman, Christine Liebrecht, Antal van den Bosch, Margot van Mulken


Abstract
Verbal irony, or sarcasm, presents a significant technical and conceptual challenge when it comes to automatic detection. Moreover, it can be a disruptive factor in sentiment analysis and opinion mining, because it changes the polarity of a message implicitly. Extant methods for automatic detection are mostly based on overt clues to ironic intent such as hashtags, also known as irony markers. In this paper, we investigate whether people who know each other make use of irony markers less often than people who do not know each other. We trained a machinelearning classifier to detect sarcasm in Twitter messages (tweets) that were addressed to specific users, and in tweets that were not addressed to a particular user. Human coders analyzed the top-1000 features found to be most discriminative into ten categories of irony markers. The classifier was also tested within and across the two categories. We find that tweets with a user mention contain fewer irony markers than tweets not addressed to a particular user. Classification experiments confirm that the irony in the two types of tweets is signaled differently. The within-category performance of the classifier is about 91% for both categories, while cross-category experiments yield substantially lower generalization performance scores of 75% and 71%. We conclude that irony markers are used more often when there is less mutual knowledge between sender and receiver. Senders addressing other Twitter users less often use irony markers, relying on mutual knowledge which should lead the receiver to infer ironic intent from more implicit clues. With regard to automatic detection, we conclude that our classifier is able to detect ironic tweets addressed at another user as reliably as tweets that are not addressed at at a particular person.
Anthology ID:
2016.lilt-14.7
Volume:
Linguistic Issues in Language Technology, Volume 14, 2016 - Modality: Logic, Semantics, Annotation, and Machine Learning
Month:
Sept
Year:
2016
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LILT
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Publisher:
CSLI Publications
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URL:
https://www.aclweb.org/anthology/2016.lilt-14.7
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http://aclanthology.lst.uni-saarland.de/2016.lilt-14.7.pdf