This paper introduces a comprehensive methodology for conducting sentiment analysis on social media using advanced deep learning techniques to address the unique challenges of this domain. As digital platforms play an increasingly pivotal role in shaping public discourse, the demand for real-time sentiment analysis has expanded across various sectors, including policymaking, brand monitoring, and personalized services. Our study details a robust framework that encompasses every phase of the deep learning process, from data collection and preprocessing to feature extraction and model optimization. We implement sophisticated data preprocessing techniques to improve data quality and adopt innovative feature extraction methods such as TF-IDF, Word2Vec, and GloVe. Our approach integrates several advanced deep learning configurations, including variants of BiLSTMs, and employs tools like Scikit-learn and Gensim for efficient hyperparameter tuning and model optimization. Through meticulous optimization with GridSearchCV, we enhance the robustness and generalizability of our models. We conduct extensive experimental analysis to evaluate these models against multiple configurations using standard metrics to identify the most effective techniques. Additionally, we benchmark our methods against prior studies, and our findings demonstrate that our proposed approaches outperform comparative techniques. These results provide valuable insights for implementing deep learning in sentiment analysis and contribute to setting benchmarks in the field, thus advancing both the theoretical and practical applications of sentiment analysis in real-world scenarios.