Social Media Sentiment Analysis for Sustainable Rural Event Planning: A Case Study of Agricultural Festivals in Al-Baha, Saudi Arabia DOI Open Access
Musaad Alzahrani, Fahad AlGhamdi

Sustainability, Journal Year: 2025, Volume and Issue: 17(9), P. 3864 - 3864

Published: April 25, 2025

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

Language: Английский

Social Media Sentiment Analysis for Sustainable Rural Event Planning: A Case Study of Agricultural Festivals in Al-Baha, Saudi Arabia DOI Open Access
Musaad Alzahrani, Fahad AlGhamdi

Sustainability, Journal Year: 2025, Volume and Issue: 17(9), P. 3864 - 3864

Published: April 25, 2025

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

Language: Английский

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