a Proposed Machine learning model for predicting Egyptian Parliament Election Results - doaa alkhiary; Samir Saleh; Mohamd Marie (Utan Kayu Publishins, 2025)
Political life and election have become one of the most important comments on social media sites. Governments have shown a keen interest in predicting the results of elections, whether presidential or parliamentary. The purpose of this study is to predict the results of the Egyptian Parliament elections using sentiment analysis, specifically Support Vector Machines (SVM), Naive Bayes, Decision Trees, and Random Forests in the context of machine learning. In this study, a sentiment analysis approach is employed to analyze public sentiment towards political parties and candidates leading up to Parliament elections. The sentiment analysis techniques are utilized to classify sentiment from textual data collected from Tweeter; Data were obtained in November 2020 before and during election days. The study utilizes a machine learning framework to train and test the models using a labeled dataset of sentiment-labeled political texts. The findings of this study reveal that sentiment analysis using machine learning can effectively predict the results of Parliament elections. The accuracy and performance of each technique are evaluated and compared to determine the most accurate and reliable predictor of election outcomes. This study contributes to the existing literature by applying sentiment analysis techniques to predict Parliament election results. The use of machine learning algorithms in combination with sentiment analysis, offers a novel approach to election forecasting. The findings of this study can be valuable for political analysts, election strategists, and policymakers seeking to understand public sentiment and predict election outcomes accurately.