{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:41:38Z","timestamp":1759970498843,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T00:00:00Z","timestamp":1737590400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Understanding public sentiment on health and fitness is essential for addressing regional health challenges in Saudi Arabia. This research employs sentiment analysis to assess fitness awareness by analyzing content from the X platform (formerly Twitter), using a dataset called Saudi Aware, which includes 3593 posts related to fitness awareness. Preprocessing steps such as normalization, stop-word removal, and tokenization ensured high-quality data. The findings revealed that positive sentiments about fitness and health were more prevalent than negative ones, with posts across all sentiment categories being most common in the western region. However, the eastern region exhibited the highest percentage of positive sentiment, indicating a strong interest in fitness and health. For sentiment classification, we fine-tuned two transformer architectures\u2014BERT and GPT\u2014utilizing three BERT-based models (AraBERT, MARBERT, CAMeLBERT) and GPT-3.5. These findings provide valuable insights into Saudi Arabian attitudes toward fitness and health, offering actionable information for public health campaigns and initiatives.<\/jats:p>","DOI":"10.3390\/bdcc9020020","type":"journal-article","created":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T04:54:31Z","timestamp":1737608071000},"page":"20","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fit Talks: Forecasting Fitness Awareness in Saudi Arabia Using Fine-Tuned Transformers"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3311-3119","authenticated-orcid":false,"given":"Nora","family":"Alturayeif","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0265-4633","authenticated-orcid":false,"given":"Deemah","family":"Alqahtani","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8246-4658","authenticated-orcid":false,"given":"Sumayh S.","family":"Aljameel","sequence":"additional","affiliation":[{"name":"Aramco Saudi Accelerated Innovation Lab (aramcoSAIL), Saudi Aramco, Dhahran 31311, Saudi Arabia"}]},{"given":"Najla","family":"Almajed","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}]},{"given":"Lama","family":"Alshehri","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}]},{"given":"Nourah","family":"Aldhuwaihi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}]},{"given":"Madawi","family":"Alhadyan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}]},{"given":"Nouf","family":"Aldakheel","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1007\/s12065-020-00429-1","article-title":"An efficient approach for sentiment analysis using machine learning algorithm","volume":"14","author":"Naresh","year":"2021","journal-title":"Evol. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kavitha, M., Naib, B.B., Mallikarjuna, B., Kavitha, R., and Srinivasan, R. (2022, January 28\u201329). Sentiment analysis using NLP and machine learning techniques on social media data. 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