The stock market plays a crucial role in shaping the global economy, yet the factors influencing its fluctuations remain a subject of ongoing analysis. In recent years, major stock prices have been increasingly impacted by public figures’ opinions shared on social media. These posts, which can be collected and analyzed without explicit consent, present an opportunity for stock market prediction using Sentiment Analysis. However, this raises ethical concerns and questions about the practicality of such an approach. Our research addresses these issues by utilising a widely recognized Kaggle dataset containing tweets about 25 publicly traded companies. After preprocessing the data, we systematically experimented with various classification algorithms and a transformer model. Our findings revealed that the pretrained bidirectional DistilBERT model achieved the highest accuracy at 82.79%. By visualizing the results and comparing them to actual stock prices, we demonstrate the feasibility of leveraging sentiment analysis for stock market predictions based on publicly available data.