Spam and Sentiment Detection in Arabic Tweets Using MARBERT Model

Researchers developed a sentiment analysis model using MARBERT to classify Arabic tweets for Saudi Telecom Company (STC), training on 24,513 tweets across five sentiment categories. The study addresses a significant gap in NLP research by applying advanced transformer-based models to Arabic social media data, enabling improved customer service through automated sentiment detection.

This research tackles a genuine challenge in natural language processing: the underrepresentation of non-English languages in sentiment analysis studies. While BERT and transformer models have revolutionized English NLP tasks, Arabic-language applications remain sparse despite the language's widespread use across the Middle East and North Africa. The study's focus on Saudi Telecom Company provides a practical business case for deploying such technology at scale, where customer feedback on social platforms directly influences service quality and brand reputation.

The dataset composition reveals important real-world constraints—the imbalanced distribution across sentiment categories (13,828 negative tweets versus 1,437 positive ones) reflects authentic customer communication patterns rather than synthetic balanced data. This imbalance presents both methodological challenges and opportunities for developing robust models that handle skewed distributions common in production environments.

For the enterprise software and AI sectors, this work demonstrates growing demand for localized NLP solutions in emerging markets. Companies operating in Arabic-speaking regions face genuine friction when forced to rely on English-trained models that fail to capture linguistic nuances, cultural context, and local sentiment markers. The application to customer service automation holds particular value, as automated sentiment classification can route high-priority complaints to human agents while identifying trends in customer dissatisfaction.

The broader implication extends beyond STC—successful implementation of MARBERT for Arabic sentiment analysis validates a scalable approach for other non-English languages, potentially spurring investment in multilingual NLP tools. However, the research remains preliminary; real-world deployment metrics and comparative performance against existing Arabic NLP solutions would strengthen claims of superiority.

• →MARBERT-based sentiment analysis addresses a critical gap in Arabic-language NLP research with practical business applications for customer service improvement.
• →The study's real-world dataset of 24,513 STC tweets reveals significantly imbalanced sentiment distribution, reflecting authentic customer communication patterns.
• →Transformer models trained on Arabic social media data achieve competitive performance metrics, validating approaches for non-English language processing.
• →Automated sentiment detection on social platforms enables companies to prioritize customer issues and identify service improvement opportunities at scale.
• →Success in Arabic NLP applications signals growing market demand for localized AI solutions in non-English speaking regions.