Abstract
Sentiment analysis is a powerful tool for transforming consumer feedback into actionable insights, enabling businesses to refine strategies and improve customer experiences. This study evaluates the performance of machine learning models, including Logistic Regression, Random Forest, SVM, LSTM, and BERT, for sentiment classification on a diverse dataset of customer reviews. BERT outperformed other models, achieving an AUC-ROC of 0.97 and an accuracy of 94.2%, showcasing its ability to capture complex semantic patterns in text. The findings provide businesses with critical insights into consumer sentiment, guiding decision-making and enhancing competitive advantage. The study also addresses challenges such as data ambiguity, ethical considerations, and computational demands, offering practical recommendations for implementing scalable and effective sentiment analysis solutions. These results demonstrate the potential of machine learning-driven sentiment analysis in shaping customer-focused business strategies and fostering growth in a data-driven market.
Published Version
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