Deep learning-based attention models for sarcasm detection in text

Abstract

Finding sarcastic statements has recently drawn a lot of curiosity in social media, mainly because sarcastic tweets may include favorable phrases that fill in unattractive or undesirable attributes. As the internet becomes increasingly ingrained in our daily lives, many multimedia information is being produced online. Much of the information recorded mostly on the internet is textual data. It is crucial to comprehend people's sentiments. However, sarcastic content will hinder the effectiveness of sentiment analysis systems. Correctly identifying sarcasm and correctly predicting people's motives are extremely important. Sarcasm is particularly hard to recognize, both by humans and by machines. We employ the deep bi-directional long-short memory (Bi-LSTM) and a hybrid architecture of the convolution neural network+Bi-LSTM (CNN+Bi-LSTM) with attention networks for identifying sarcastic remarks in a corpus. Using the SarcasmV2 dataset, we test the efficacy of deep learning methods BiLSTM, and CNN+BiLSTM with attention network) for the task of identifying text sarcasm. The suggested approach incorporating deep networks is consistent with various recent and advanced techniques for sarcasm detection. With attention processes, the improved CNN+Bi-LSTM model achieved an accuracy rate of 91.76%, which is a notable increase over earlier research.