Sentiment Analysis Revisited: A Multi-Metric Comparative Study

Sentiment analysis is a fundamental task in natural language processing with wide-ranging applications, from customer feedback monitoring to healthcare and social media analytics. While recent research has mainly emphasized predictive accuracy, computational efficiency has remained largely overlooked, despite its importance for large-scale and real-time deployment. This study addresses this gap by conducting a comparative evaluation of classical machine learning algorithms (Logistic Regression, Naïve Bayes, Random Forest) and deep learning architectures [Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM)]. Experiments were carried out on two benchmark datasets, IMDB and Yelp Polarity, with evaluation based on accuracy, precision, recall, F1-score, training time, and a novel Efficiency Score. Results on IMDB show that Logistic Regression and LSTM both achieved 88% accuracy, but with radically different costs: Logistic Regression trained in 0.25 seconds, whereas LSTM required more than 2600 seconds. On Yelp Polarity, Logistic Regression improved to 91.6% accuracy, outperforming LSTM (86.2%) while remaining over 300 times faster. By integrating both predictive metrics and efficiency measures, the Efficiency Score highlighted the practical advantages of Logistic Regression and Naïve Bayes in resource-constrained environments. This dual evaluation framework demonstrates that classical models remain highly competitive when both accuracy and efficiency are considered, providing a practical alternative to computationally expensive neural architectures and offering practitioners clear guidelines for model selection under real-world constraints.