The increasing prevalence of social media has led to an unprecedented volume of user-generated reviews on platforms such as Twitter, Facebook, and Yelp. Extracting actionable insights from this data requires automated sentiment analysis to classify opinions as positive, negative, or neutral. This paper presents a comprehensive sentiment analysis framework for social media reviews that leverages state-of-the-art natural language processing (NLP) and machine learning techniques. We compare classical machine learning (ML) classifiers (SVM, Naïve Bayes) using Bag-of-Words and TF-IDF features with deep learning (DL) models including CNN, BiLSTM, and Transformer-based embeddings (BERT). The proposed hybrid model employs a pre-trained BERT encoder followed by a bidirectional LSTM and a dense classifier. Experiments on benchmark datasets (Twitter posts, Yelp restaurant reviews, IMDb movie reviews) demonstrate that the BERT+BiLSTM model substantially outperforms baseline methods. We report accuracy improvements of approximately 5–10% over traditional models, achieving up to 92–94% accuracy on balanced binary review datasets. Our contributions include (1) a detailed comparison of text representation techniques, (2) a novel hybrid classification pipeline, and (3) empirical evaluation on multiple datasets. Future work will explore multilingual and aspect-based extensions.