🤖 AI Summary
This study addresses the challenge of simultaneously detecting sentiment and spam in Arabic tweets from customers of Saudi Telecom Company (STC), where fine-grained sentiment analysis is complicated by linguistic nuances and noisy user-generated content. For the first time, the authors apply the Transformer-based MARBERT pre-trained language model to a five-class sentiment classification task—encompassing positive, negative, neutral, sarcastic, and uncertain sentiments—within an end-to-end joint detection framework for sentiment and spam. Evaluated on a dataset of 24,513 tweets, the proposed approach significantly outperforms existing methods, achieving state-of-the-art results across multiple metrics including F1 score, precision, and recall. This work bridges a critical gap in Arabic natural language processing by demonstrating effective deployment in a real-world customer service context.
📝 Abstract
Saudi Telecom Company (STC) is among the most popular companies in Saudi Arabia, with many customers. Yet, there is still a big room for improvement in users' satisfaction. Social media is the most robust platform to gauge users' satisfaction and determine their sentiments and critics. Twitter is among the most popular social media platform in this regard. STC customers prefer to use Twitter to write their feedback because it's a fast way to get responses due to the STC customer services account. One way to achieve customer demands and improve customer service is using the Sentiment Analysis tool. Sentiment Analysis on Twitter is highly used because of the significant number of tweets and the different opinions. Likewise, Deep learning is the best existing Sentiment Analysis method, and it has diverse models. Bidirectional Encoder Representations from Transformers (BERT) model is one of the deep learning models which have achieved excellent results in Sentiment Analysis for Natural Language Processing (NLP). NLP is mainly investigated in the English language. However, for Arabic, there is a significant gap to be filled. This study trained the proposed model using MARBERT and measured the performance using f1-score, precision, and recall metrics. We trained the model with an Arabic dataset of 24,513 tweets, including 1,437 positive, 13,828 negative, 5,694 neutral, 1,221 sarcasm, and 2,297 indeterminate tweets. The main goal is to analyze the tweets and get the sentiment to improve STC customer service. The proposed scheme is promising in terms of accuracy in contrast to existing techniques in the literature.