Social Media Sentiment Analysis for Sustainable Rural Event Planning: A Case Study of Agricultural Festivals in Al-Baha, Saudi Arabia

Agricultural festivals play a vital role in promoting sustainable farming, local economies, and cultural heritage. Understanding public sentiment toward these events can provide valuable insights to enhance event organization, marketing strategies, and economic sustainability. In this study, we collected and analyzed social media data from Twitter to evaluate public perceptions of Al-Baha’s agricultural festivals. Sentiment analysis was performed using both traditional machine learning and deep learning approaches. Specifically, six machine learning models including Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), k-Nearest Neighbors (KNN), and XGBoost (XGB) were compared against AraBERT, a transformer-based deep learning model. Each model was evaluated based on accuracy, precision, recall, and F1-score. The results demonstrated that AraBERT achieved the highest performance across all metrics, with an accuracy of 85%, confirming its superiority in Arabic sentiment classification. Among traditional models, SVM and RF performed best, whereas MNB and KNN struggled with sentiment detection. These findings highlight the role of sentiment analysis in supporting sustainable agricultural and tourism initiatives. The insights gained from sentiment trends can help festival organizers, policymakers, and agricultural stakeholders make data-driven decisions to enhance sustainable event planning, optimize resource allocation, and improve marketing strategies in line with the Sustainable Development Goals (SDGs).