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Sarcasm is very topic-dependent and highly contextual. Pre-trained sentiment, emotion, and personality models are used to capture contextualized information from text. Hand-crafted features somewhat useful for sarcasm detection.
Detection of Sarcasm in Text Data using Deep Convolutional Neural Networks: 1. Skip gram technique to convert words to vectors. 2. Discover patterns from the textual datasets based on the identifiable features. 3. The approach is giving an overall accuracy of 89.9%.
Contextual SarCasm Detector (CASCADE), which adopts a hybrid approach of both content and context-driven modeling for sarcasm detection in online social media discussions.
The paper addresses a key NLP task known as sarcasm detection using a combination of model based on convolutional neural networks (CNNs). Detection of sarcasm is important in other areas such as affective computing and sentiment analysis because such expressions can flip the polarity of a sentence.Example Sarcasm can be considered as expressing a bitter gibe or taunt. Examples include statements such as Is it time for your medication or mine? and I work 40 hours a week to be this poor. Challenges to understand and detect sarcasm it is important to understand the facts related to an event.