Sentiment analysis is one of the most important tasks in natural language processing, as it allows opinions expressed in text to be automatically categorized. This study presents a systematically controlled comparative analysis of classical machine-learning, deep-learning, feature-fusion, and fully fine-tuned transformer models for binary sentiment classification on the Internet Movie Database (IMDb) Movie Reviews dataset. While the individual components of the framework, including Term Frequency-Inverse Document Frequency (TF-IDF), Bidirectional Encoder Representations from Transformers (BERT) embeddings, feature fusion, and Multi-Layer Perceptron (MLP) classification, are established techniques, this study contributes a comprehensive empirical evaluation of their combined effectiveness for the target classification task, providing insights into the practical benefits of integrating lexical and contextual representations. Under a single experimental framework, all models are evaluated on a commonly held-out test set with 95% bootstrap confidence intervals, paired McNemar significance testing, confusion-matrix and error analysis, and a computational-complexity comparison. The fully fine-tuned transformers achieve the highest performance Robustly Optimized BERT Pretraining Approach (RoBERTa), F1-Score = 0.9427. The TF-IDF and BERT feature-fusion model is significantly more accurate than the classical, deep-learning, and BERT-only baselines (all p < 0.001), but significantly less accurate than the fine-tuned transformers (all p < 0.01); furthermore, concatenating the [CLS] token and mean-pooled representations does not significantly improve upon mean pooling alone ( p = 0.48). The study provides a transparent, evidence-based account of the accuracy and cost trade-offs among these model families.