Saudi Telecom Company (STC) aims to enhance user satisfaction by leveraging Twitter feedback for sentiment analysis. The study addresses the gap in Arabic Natural Language Processing by training the MARBERT model on a specific dataset of 24,513 tweets. This collection includes 1,437 positive, 13,828 negative, and 5,694 neutral tweets, alongside 1,221 sarcastic and 2,297 indeterminate entries. The primary objective is to analyze these sentiments to improve STC's customer service responsiveness. Performance was evaluated using f1-score, precision, and recall metrics to ensure robust detection of spam and sentiment. Results indicate that the proposed scheme offers promising accuracy compared to existing techniques in the literature.