IDEAS home Printed from https://ideas.repec.org/a/wsi/fracta/v32y2024i09n10ns0218348x25400316.html
   My bibliography  Save this article

Automated Sarcasm Recognition Using Applied Linguistics Driven Deep Learning With Large Language Model

Author

Listed:
  • 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)

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.

Suggested Citation

  • Abdulkhaleq Q. A. Hassan & Shoayee Dlaim Alotaibi & Wala Bin Subait & Abdullah Saad Al-Dobaian & Hanan Al Sultan & Manar Almanea & Randa Allafi & Menwa Alshammeri, 2024. "Automated Sarcasm Recognition Using Applied Linguistics Driven Deep Learning With Large Language Model," FRACTALS (fractals), World Scientific Publishing Co. Pte. Ltd., vol. 32(09n10), pages 1-11.
  • Handle: RePEc:wsi:fracta:v:32:y:2024:i:09n10:n:s0218348x25400316
    DOI: 10.1142/S0218348X25400316
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0218348X25400316
    Download Restriction: Access to full text is restricted to subscribers

    File URL: https://libkey.io/10.1142/S0218348X25400316?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wsi:fracta:v:32:y:2024:i:09n10:n:s0218348x25400316. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tai Tone Lim (email available below). General contact details of provider: https://www.worldscientific.com/worldscinet/fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.