Sentiment Analysis Using Machine Learning Model for Qatar World Cup 2022 among Different Arabic Countries Using Twitter API

The World Cup is a global event that attracts much attention on social media, with millions of people around the world sharing their opinions and sentiments about the tournament on platforms like Twitter. In recent years, there has been a growing interest in using sentiment analysis to understand and analyze the attitudes and opinions expressed in social media data related to the World Cup. The objective of this research paper is to explore the opinion of Qatar World Cup 2022 among Twitter users in Arabic countries (Egypt - Oman - Syria - Palestine - Algeria - Kuwait - Iraq - Sudan - Iraq - Sudan - Saudi - Jordon - Bahrain - Qatar - Yemen -Emirates) using a machine learning algorithm. To achieve this objective, we created a new dataset. Four algorithms were employed in this study: logistic regression, random forest classifier, Naive Bayes classifier, and support vector machine classifier. The best average accuracy of logistic regression was 93%, the average accuracy of the Random Forest Classifier was 92%, the average accuracy of the Naive Bayes Classifier was 88%, and the average accuracy of the Support Vector Machine-Classifier was 93%.