The expanding realm of technological advancements has paved the way for novel human-machine interaction possibilities. Amid the COVID-19 pandemic, shifts in communication patterns, particularly the surge in social media engagement (e.g., X platform), have inundated platforms with tweets expressing diverse sentiments toward the virus and its mitigation measures, including vaccinations. This wealth of data presents a ripe opportunity for sentiment analysis, not only for understanding public responses but also for informing effective decision-making processes. To navigate this vast expanse of tweets and distill meaningful insights, we advocate a novel approach that integrates the strengths of GLOVE, LSTM, and deep learning techniques. By leveraging GLOVE embeddings, our methodology captures the nuanced semantics of tweets, enhancing the accuracy of sentiment analysis. LSTM networks, trained on diverse datasets, decode the temporal dynamics of language usage, providing a comprehensive understanding of evolving sentiments. Deep learning techniques further bolster our approach, enabling the extraction of intricate patterns and relationships within the data. Through social media analysis and data mining in healthcare, our methodology aims to shed light on the multifaceted aspects of public opinion surrounding the pandemic. Our integrated approach not only facilitates sentiment analysis but also lays the groundwork for proactive healthcare strategies. By harnessing the power of machine learning, we can identify emerging trends and sentiment shifts in real time, enabling policymakers to tailor communication strategies and interventions accordingly. In this way, our multidisciplinary approach transcends traditional sentiment analysis, offering a holistic understanding of public sentiment and its implications for healthcare decision-making in the age of social media and data abundance.