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- W3022164678 abstract "Sarcasm is the clever use of words that clearly mean the opposite of what they say, in order to criticize something in a humorous and often rude manner. Sarcasm is usually context-dependent. However, another form of sarcasm which depends on the semantics and structure of the sentence is known as linguistic sarcasm. Our work is an attempt to detecting linguistic sarcasm in tweets. We first use a Natural Language Processing (NLP) approach where we check whether sarcastic and non-sarcastic statements have any differentiating patterns in them. The intuition behind this is that sarcastic statements tend to begin on a positive note and end on a negative one. Next, we perform this task in a more efficient manner by using a powerful machine learning (ML) tool, i.e. a Recurrent Neural Network (RNN). This work majorly focuses on comparing generative and discriminative models for sarcasm detection. As a representation of these models, we are comparing Naive Bayes and RNN." @default.
- W3022164678 created "2020-05-13" @default.
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- W3022164678 creator A5052218884 @default.
- W3022164678 date "2020-01-01" @default.
- W3022164678 modified "2023-09-23" @default.
- W3022164678 title "Sarcasm Detection on Twitter Data: Generative Versus Discriminative Model" @default.
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- W3022164678 doi "https://doi.org/10.1007/978-981-15-3242-9_24" @default.
- W3022164678 hasPublicationYear "2020" @default.
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