From Review to Practice: A Comparative Study and Decision-Support Framework for Sentiment Classification Models

Sentiment classification is a core task in natural language processing (NLP), enabling automated interpretation of opinionated text across domains, such as social media, e-commerce, and healthcare. While numerous models have been proposed—from classical machine learning algorithms to deep neural networks and transformer architectures—their adoption is often hindered by trade-offs in performance, interpretability, and computational cost. This paper presents a threefold contribution: 1) a structured review of over 30 peer-reviewed studies that compare sentiment classifiers across five analytical dimensions—accuracy, robustness, interpretability, efficiency, and context adaptability; 2) a lightweight empirical benchmark on the IMDb dataset, evaluating Naïve Bayes, linear SVM, and LSTM; and 3) a practitioner-oriented decision-support framework comprising a model selection flowchart and recommendation matrix. The experimental results show that SVM achieved the highest F1-score (0.8329), while Naïve Bayes provided strong performance with minimal training time, and LSTM underperformed under constrained conditions. We further highlight persistent challenges in benchmarking consistency, model explainability, and cross-lingual adaptability. The paper concludes with actionable future directions, including hybrid architectures, low-resource deployment strategies, and inclusive NLP systems for diverse user populations. To our knowledge, this is the first study that unifies systematic review, empirical validation, and practical decision tools in the field of sentiment classification.