LEVERAGING CORPUS LINGUISTICS AND DATA-DRIVEN DEEP LEARNING FOR TEXTUAL EMOTION ANALYSIS

Somia A. Asklany, Najla I. Al-Shathry, Abdulkhaleq Q. A. Hassan, Abdullah Saad Al-Dobaian, Manar Almanea, Ayman Ahmad Alghamdi and Mutasim Al Sadig
Additional contact information
Somia A. Asklany: Department of Computer Science and Information Technology, Faculty of Science and Arts in Turaif Northern Border University, Arar 91431, Saudi Arabia
Najla I. Al-Shathry: ��Department of Language Preparation, Arabic Language Teaching Institute, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh 11671, Saudi Arabia
Abdulkhaleq Q. A. Hassan: ��Department of English, College of Sciences and Arts at Mahayil, King Khalid University Abha, Asir, Saudi Arabia
Abdullah Saad Al-Dobaian: �Department of English Language, College of Language Sciences, King Saud University, P. O. Box 145111, Riyadh, Saudi Arabia
Manar Almanea: �Department of English, College of Languages and Translation, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia
Ayman Ahmad Alghamdi: ��Department of Arabic Teaching, Arabic Language Institute, Umm Al-Qura University, Makkah, Saudi Arabia
Mutasim Al Sadig: *Department of Computer Science, College of Science, Majmaah University, Al Majmaah 11952, Saudi Arabia

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

Abstract: Emotions have played a major part in the conversation, as they express context to the conversation. Text or words in conversation contain contextual and lexical meanings. In recent times, obtaining emotion from the text has been an attractive area of research. With the emergence of machine learning (ML) algorithms and hardware to aid the ML method, identifying emotion from the text with ML provides significant and promising solutions. The main objective of Textual Emotion Analysis (TEA) is to analyze and extract the user’s emotional states in the text. Many different Complex Systems and Deep Learning (DL) algorithms have been fast-paced developed and proved their effectiveness in several fields including audio, image, and natural language processing (NLP). This has moved researchers away from the classical ML to DL for their academic research work. This study develops a new Corpus Linguistics and Data-Driven Deep Learning for Textual Emotion Analysis (CLD3L-TEA) technique. The CLD3L-TEA technique mainly investigates the distinct types of emotions that endure in the social media text. In the CLD3L-TEA model, the raw data can be pre-processed in distinct ways. Next, a multi-weighted TF–IDF model is used to generate feature vectors. For the identification of emotions, the CLD3L-TEA technique applied a gated recurrent unit (GRU). At last, the hyperparameter range of the GRU model is executed by the Fractal Harris Hawks Optimization (HHO) model. The experimental validation of the CLD3L-TEA technique on a benchmark dataset illustrates the supremacy of this technique over recent approaches.

Keywords: Textual Emotion Analysis; Natural Language Processing; Social Media; Deep Learning; Fractal Harris Hawks Optimization; Complex Systems (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0218348X25400511

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