Fit Talks: Forecasting Fitness Awareness in Saudi Arabia Using Fine-Tuned Transformers

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—BERT and GPT—utilizing 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.