Abstract

Natural Language Processing (NLP)-driven applied linguistics for sarcasm detection includes computational models to understand and identify sarcastic expressions within text. This interdisciplinary method integrates linguistics principles with advanced NLP techniques to identify subtle and nuanced cues indicative of sarcasm correctly. It includes computational approaches like linguistic feature extraction, machine learning models, and sentiment analysis. Furthermore, deep learning (DL) algorithms, including transformers and recurrent neural networks (RNNs), hold significant potential in capturing complex linguistic nuances inherent in sarcastic expression. These approaches can learn the hierarchical representation of text, which enables capturing context dependency, which is crucial for accurately detecting sarcasm. The applications of NLP-driven applied linguistics for sarcasm detection show great potential in various domains namely social media analysis, online content moderation, and customer feedback interpretation. By automating sarcasm detection, this system can enhance communication understanding, improve sentiment analysis accuracy, and contribute to better decision-making processes in various contexts. This study develops automated Sarcasm Detection using the Artificial Hummingbird Algorithm with Deep Learning (ASD-AHADL) technique. The ASD-AHADL technique applies the optimal DL model for detecting sarcastic content. To achieve this, the ASD-AHADL technique undergoes data preprocessing and the BERT-based word embedding process at the initial stage. Followed by the ASD-AHADL technique uses attention-gated recurrent unit long short-term memory (AGRU-LSTM) for the sarcasm detection process. At last, the AHA-based parameter tuning process is involved to fine-tune the parameters based on the DL algorithm. The experimental study of the ASD-AHADL technique has been tested under a social media dataset. The outcomes indicated that the solution of the ASD-AHADL technique was significant compared to others.