Abstract
Sarcasm is a figure of speech in which the speaker says something that is outwardly unpleasant with an intention of insulting or deriding the hearer and/or a third person. Designing a model for successfully detecting sarcasm has been one of the most challenging task in the field of natural language processing (NLP) because sarcasm detection is heavily dependent on the context of the utterance/statement and sometimes, even human beings are not able to detect the underlying sarcasm in the utterance. In this chapter, we design features for detecting sarcasm using pragmatic features that take into account the context of the utterance. The approach is based on a linguistic model that describes how humans distinguish between different types of untruths. We then train various machine-learning-based classifiers and compare their accuracies.
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Chatterjee, N., Aggarwal, T., Maheshwari, R. (2020). Sarcasm Detection Using Deep Learning-Based Techniques. In: Agarwal, B., Nayak, R., Mittal, N., Patnaik, S. (eds) Deep Learning-Based Approaches for Sentiment Analysis. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-1216-2_9
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DOI: https://doi.org/10.1007/978-981-15-1216-2_9
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