Sarcasm Detection in X Data Using Node Embedding and Graph Convolutional Networks

An extensive amount of attention has been paid to the implications that the detection of sarcasm on online social networks such as Facebook, X etc. could have for the reason of sarcasm detection, sentiment analysis, content regulation, and public opinion. In light of the fact that traditional methods frequently struggle to comprehend the nuances of sarcasm, there is a growing interest in more advanced methodology that make uses of network architectures and deep learning. For the purpose of identifying instances of sarcasm in X data, this study presents a novel method that combines Graph Convolutional Networks (GCNs) and node embeddings. Our solution makes use of a generalized convolutional neural network (GCN), constructs a user-word interaction network, and incorporates word2vec for initial text embedding in order to capture both textual and contextual characteristics. Using our methodology, we demonstrate that it is superior to traditional methods in terms of both accuracy and robustness.