AUTOMATED SARCASM RECOGNITION USING APPLIED LINGUISTICS DRIVEN DEEP LEARNING WITH LARGE LANGUAGE MODEL

Abdulkhaleq Q. A. Hassan, Shoayee Dlaim Alotaibi, Wala Bin Subait, Abdullah Saad Al-Dobaian, Hanan Al Sultan, Manar Almanea, Randa Allafi and Menwa Alshammeri
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Abdulkhaleq Q. A. Hassan: Department of English, College of Science and Arts at Mahayil, King Khalid University, Saudi Arabia
Shoayee Dlaim Alotaibi: ��Department of Artificial Intelligence and Data Science, College of Computer Science and Engineering University of Hail, Saudi Arabia
Wala Bin Subait: ��Department of Language Preparation, Arabic Language Teaching Institute, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh 11671, Saudi Arabia
Abdullah Saad Al-Dobaian: �Department of English Language, College of Language Sciences, King Saud University, P. O. Box 145111, Riyadh, Saudi Arabia
Hanan Al Sultan: �Department of English, College of Arts, King Faisal University, Saudi Arabia
Manar Almanea: ��Department of English, College of Languages and Translation, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia
Randa Allafi: *Department of Computers and Information Technology, College of Sciences and Arts, Northern Border University, Arar, Saudi Arabia
Menwa Alshammeri: ��†Department of Computer Science, College of Computer and Information Sciences, Jouf University, Saudi Arabia

FRACTALS (fractals), 2024, vol. 32, issue 09n10, 1-11

Abstract: Posting sarcastic comments on social media has become popular in the modern era. Sarcasm is a linguistic expression that typically conveys the contrary meaning of what has already been said, making it challenging for machines to find the literal meaning. It depends mainly on context, making it a tedious process for computational analysis. It is well known for its modulation with spoken words and an irony undertone. In addition, sarcasm conveys negative sentiment using positive words, which easily confuses sentiment analysis (SA) models. Sarcasm detection is a natural language processing (NLP) process and is prevalent in SA, human–machine dialogue, and other NLP applications due to sarcasm’s ambiguities and complex nature. Concurrently, the advancement of machine learning (ML) techniques makes it easier to develop robust sarcasm detection methods. This paper presents an automated sarcasm recognition using applied linguistics-driven deep learning with a large language model (ASR-ALDL3M) technique. The purpose of the ASR-ALDL3M technique is to focus on recognizing the sarcastic data using the DL model. In the ASR-ALDL3M technique, the initial data preprocessing phase is utilized, and glove word embedding is applied. Next, the sarcasm recognition procedure is applied using the long short-term memory (LSTM) model. Moreover, the hyperparameter selection of the LSTM model is performed using the fractals monarch butterfly optimization (MBO) technique. At last, a large language model (LLM) is utilized to enhance the sarcastic recognition process. A comprehensive result analysis is made to validate the outcomes of the ASR-ALDL3M technique. The performance evaluation outcomes stated that the ASR-ALDL3M method gains better performance over other models.

Keywords: Sarcasm Recognition; Deep Learning; Fractals Monarch Butterfly Optimization; Large Language Model; Word Embedding (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0218348X25400316

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