Sarcasm Messages Detection using Hybrid Features Extraction Deriving from Context and Content Sentences on Social Networks

This research aims to enhance the detection of sarcastic messages in the Thai language across social networks. It involves extracting and analyzing context-based features from messages to identify and differentiate sarcastic content. The study employs deep learning and machine learning techniques to classify these messages. The experimental findings demonstrate that a combination of context-based and content-based features yields the highest accuracy in identification. Specifically, the utilization of a bidirectional Long-Short Term Memory (Bi-LSTM) with 256 nodes, ReLU as the activation function, a dropout rate of 0.2, Sigmoid as the output activation function, binary cross-entropy as the loss function, and the Adam optimizer resulted in the highest accuracy achieved by the Bi-LSTM model, reaching 96.79%.