Social media platforms have become major venues for public expression, making the analysis of sentiments within these data streams increasingly valuable for applications ranging from consumer feedback to public health monitoring. This paper explores sentiment analysis using Bidirectional Encoder Representations from Transformers (BERT) and Long Short-Term Memory (LSTM) models, assessing their performance for accurately capturing emotional nuances in social media text. We conducted an extensive literature review to understand current approaches, identify gaps in the existing methods, and establish research questions focused on improving model efficiency and accuracy in handling diverse social media data. The key findings presented in this chapter highlight the strengths plus the limitations of recent methodologies, offering insights into future research directions. Our study provides a framework for advancing sentiment analysis, aiming to contribute a robust approach to interpreting social media sentiments effectively.