Robust Sarcasm Detection using Artificial Rabbits Optimizer with Multilayer Convolutional Encoder-Decoder Neural Network on Social Media

Abstract

Nowadays, posting sarcastic comments on media platforms developed a general trend. People to pester or taunt others frequently utilize sarcasm. It is regularly stated that with tonal stress, inflexion from the speech or in the procedure of hyperbolic, lexical, and pragmatic aspects occur from the textual data. Sarcasm Detection (SD) utilizing Deep Learning (DL) on media platforms is an active study field in Natural Language Processing (NLP). Sarcasm is a figurative language method frequently exploited on social networks like Reddit, Twitter, and Facebook. Detecting sarcasm is essential to various applications like Sentiment Analysis (SA), opinion mining, and social network monitoring. DL techniques are demonstrated that effectual at sarcasm detection on media platforms. This study presents a robust sarcasm detection using Artificial Rabbits Optimizer with Multilayer Convolutional Encoder-Decoder Neural Network (ARO-MCEDNN) technique on social media—the presented ARO-MCEDNN technique concentrations on detecting sarcasm in social networking sites. Primarily, the ARO-MCEDNN technique follows a series of pre-processing data levels for transforming the input data into a compatible format. Followed by, Glove approach is applied for word embedding purposes. Moreover, the MCEDNN model is applied as a classification model to identify and categorize distinct kinds of sarcasm. Furthermore, the ARO algorithm is chosen as a hyperparameter optimizer of the MCEDNN model, enhancing the sarcasm detection performance. To highlight the advanced performance of the ARO-MCEDNN system, a sequence of simulations was performed.