Automated sarcasm detection and classification using hyperparameter tuned deep learning model for social networks

Abstract

In recent digital era, social media sites have been commonly used by majority of people to generate massive quantities of textual data. Sarcasm can be treated as a kind of sentiment, which generally expresses the opposite of what has been anticipated. Since sarcasm detection is mainly based on the context of utterances or sentences, it is hard to design a model to proficiently detect sarcasm in the domain of natural language processing (NLP). The recent advancements of deep learning (DL) models influence neural networks (NN) in learning the lexical as well as contextual features, eradicating the necessity of hand-crafted features for sarcasm detection. With this motivation, this article designs an automated sarcasm detection and classification tool using hyperparameter tuned deep learning (ASDC-HPTDL) model for social media. The proposed ASDC-HPTDL technique primarily involves pre-processing stage to transform the data into useful format. At the next stage of pre-processing, the pre-processed data is converted into the feature vector by Glove Embedding's technique. Followed by, attention bidirectional gated recurrent unit (ABiGRU) technique is utilized to detect and classify sarcasm. In order to boost the detection outcomes of the ABiGRU technique, a hyperparameter tuning process using improved artificial flora algorithm (IAFO) is employed, shows the novelty of the work. The proposed model is validated using the benchmark dataset and the results are examined interms of precision, recall, accuracy, and F1-score.

Text-based Sarcasm Detection on Social Networks: A Systematic Review

“…The basic pre-processing steps are performed on the original text before the classification. Further, in [54], the researchers proposed a new attention-based BiGRU for detecting sarcasm in which hyper parameter tuning is www.ijacsa.thesai.org performed using an artificial flora algorithm and embedding is performed by the GloVe model. Very few works have utilized an ensemble of ML and DL approaches.…”

Enhanced Deep Learning-Based Model for Sentiment Analysis to Identify Sarcasm Appeared in the News

“…Vinoth and Prabhavathy [16] developed an automated sarcasm detection and classification tool for social media utilizing an ASDC-HPTDL model for hyperparameter-optimized deep learning. To recognize and categorize sarcasm, the attention bidirectional gated recurrent unit (ABiGRU) technique and improved artificial flora algorithm (IAFO) hyper-parameter tuning method were employed.…”

Special issue on International Conference on Computing and Communication Networks

“…Vinoth & Prabhavathy, 2022 propose an automated sarcasm detection and classification tool using hyperparameter tuned deep learning (ASDC‐HPTDL) model for social media. The proposed ASDC‐HPTDL technique primarily involves pre‐processing stage to transform the data into useful format.…”