<![CDATA[This study analyzes the relationship between public sentiment on the social media platform X and Bitcoin's global price volatility from January to October 2024. Using sentiment analysis supported by the BERT machine learning model and the Support Vector Machine (SVM) algorithm, relevant tweets were classified into positive, neutral, and negative sentiments. Model evaluation demonstrated excellent performance, with precision, recall, and F1-score for positive sentiment reaching 95.52%, 93.57%, and 94.53%, respectively. Neutral sentiment achieved precision of 88.61%, recall of 92.11%, and an F1-score of 90.32%. Negative sentiment yielded precision of 92.02%, recall of 91.05%, and an F1-score of 91.53%. The results indicate a significant correlation between public sentiment and Bitcoin price movements, where positive sentiment drives price increases while negative sentiment often triggers sell-offs. Moreover, the intensity of social media discussions significantly impacts market dynamics, as evidenced by a spike in activity in March 2024 coinciding with Bitcoin's price peak during the study period. These findings provide insights for investors, market analysts, and regulators to understand the role of social media as a market sentiment indicator influencing digital asset volatility.]]>
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