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This study conducts a comparative sentiment analysis of over 700 Starbucks customer reviews using both classical machine learning and deep learning techniques. By binarizing star ratings to classify sentiments and addressing class imbalance, the research identifies that Support Vector Machine (SVM) achieved the highest accuracy at 91.0%, while Bidirectional LSTM outperformed other deep learning models in generalization on unseen data. The results underscore the critical impact of model selection and preprocessing on sentiment analysis in the retail coffee sector, particularly in enhancing positive sentiment recall.
Class imbalance in consumer reviews can significantly hinder positive sentiment detection, but SVM and Bidirectional LSTM offer robust solutions for accurate sentiment analysis.
Consumer reviews play an important role in shaping brand perception and business strategies, particularly in service-driven industries such as retail coffee. This study presents a comparative sentiment analysis framework for Starbucks customer reviews using classical machine learning and deep learning approaches. The dataset, collected from ConsumerAffairs, contains more than 700 reviews and was analyzed through preprocessing and exploratory data analysis to identify temporal and geographic patterns. Sentiment labels were generated by binarizing star ratings, with ratings of 4 and 5 classified as positive and ratings of 1 to 3 as negative. The resulting dataset was substantially imbalanced toward negative sentiment. Five machine learning classifiers, including Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, and Naive Bayes, were evaluated alongside five deep learning models: LSTM, RNN, Bidirectional LSTM, GRU, and CNN. Model performance was assessed using accuracy, precision, recall, and F1-score. SVM achieved the highest accuracy among the machine learning models at 91.0 percent, while Bidirectional LSTM showed the strongest performance among the deep learning models and demonstrated good generalization on unseen data. The findings also show that class imbalance negatively affected positive sentiment recall across several models. Overall, this study provides a comparative evaluation of machine learning and deep learning approaches for real-world consumer sentiment analysis and highlights the importance of appropriate model selection and preprocessing for customer experience analytics in the retail coffee sector.
