Twitter Sentiment Analysis of the Arabic Language.

Sentiment analysis is commonly employed in the field of social communication to measure people's feelings on a variety of topics because of the large amount of data that these platforms provide. Given that the tweet is published in a range of no more than 280 characters, the Twitter platform has been very popular and good for developing and presenting thoughts, and so facilitates the process of sentiment analysis. In contrast to Arabic, the English language has garnered a lot of attention from sentiment analysis on most social media platforms. This article examines Arabic-language Twitter emotions, comparing four machine learning models and determining which one has the best accuracy. The findings were analyzed using precision, recall, f1-score, accuracy, confusion matrix, and ROC-AUC curve. The four models' accuracy ranged from 62% to 89%.