Sentiment Analysis of Arab Tweets: Unveiling Public Opinion Trends Using Machine Learning

Abstract

Abstract: The rapid spread of social media platforms, especially Twitter, has provided a rich and valuable source of data for understanding public opinion. This thesis presents a comprehensive study on analyzing the sentiments of Arab tweeters, intending to reveal public opinion trends using advanced machine learning techniques. The study begins by collecting a large data set of tweets in Arabic. Preprocessing steps are carefully implemented to deal with the unique linguistic characteristics of Arabic, including encoding, normalization, and deletion. The BERT system was used for the application and the model was created using AJGT.csv Dataset. The evaluation was performed using different metrics. The test results were as follows: 0.9822 % accuracy, precision 0.9823, recall 0.9822, F1 0.982265 and FPR 0.0177. The findings of this research provide significant insights into the prevailing public opinion trends in the Arab world, revealing the potential of sentiment analysis as a powerful tool for policymakers, businesses, and researchers to gauge public sentiment and make informed decisions. The thesis concludes with a discussion of the limitations of the study and suggestions for future research directions, emphasizing the need for more sophisticated models and larger datasets to further enhance the accuracy and reliability of sentiment analysis in the Arabic language.