Abstract: Sentiment Analysis (SA) and Emotion Analysis (EA) are effective areas of research aimed to auto-detect and recognize the sentiment expressed in a text and identify the underpinning opinion towards a specific topic. Although they are often considered interchangeable terms, they have slight differences. The primary purpose of SA is to find the polarity expressed in a text by distinguishing between positive, negative, and neutral opinions. EA is concerned with detecting more emotion categories, such as happiness, anger, sadness, and fear. EA allows the analysis to extract more accurate and detailed results that suit the field in which it is applied. This work delves into EA within the Saudi Arabian dialect, focusing on sentiments related to COVID-19 vaccination campaigns. Our endeavor addresses the absence of research on developing an effective EA machine-learning model for Saudi dialect texts, particularly within the healthcare and vaccinations domain, exacerbated by the lack of an EA manual-labeled corpus. Using a systematic approach, a dataset of 33,373 tweets is collected, annotated, and preprocessed. Thirty-six machine learning experiments encompassing SVM, Logistic Regression, Decision Tree models, three stemming techniques, and four feature extraction methods enhance the understanding of public sentiment surrounding COVID-19 vaccination campaigns. Our Logistic Regression model achieved 74.95% accuracy. Findings reveal a predominantly positive sentiment, particularly happiness, among Saudi citizens. This research contributes valuable insights for healthcare communication, public sentiment monitoring, and decision-making while providing labeled-corpus and ML model comparison results for improving model performance and exploring broader linguistic and dialectal applications.

Abdulrahman O. Mostafa and Tarig M. Ahmed, “Enhanced Emotion Analysis Model using Machine Learning in Saudi Dialect: COVID-19 Vaccination Case Study” International Journal of Advanced Computer Science and Applications(IJACSA), 15(1), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150134

@article{Mostafa2024,
title = {Enhanced Emotion Analysis Model using Machine Learning in Saudi Dialect: COVID-19 Vaccination Case Study},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150134},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150134},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {1},
author = {Abdulrahman O. Mostafa and Tarig M. Ahmed}
}