Unveiling the impact of service attributes and review scores on sentiment: a deep learning and feature engineering approach to UberEats reviews

This study investigates the impact of SERVQUAL dimensions (Assurance, Reliability, Tangibles, Empathy, and Responsiveness) and review scores on customer sentiment. We analyze a large dataset of 920,407 UberEats reviews from the Google Play Store, classifying sentiment based on star ratings and using a Long Short-Term Memory (LSTM) model to predict sentiment from review content. Using text mining and sentiment analysis, the study employs robust feature engineering techniques to extract and quantify SERVQUAL components from customer reviews. The LSTM model demonstrated high accuracy (89.64%) in predicting sentiment, validating the alignment between predicted and assigned sentiments. Our analysis reveals that all SERVQUAL dimensions and review scores have a positive and significant impact on overall sentiment. Specifically, the Ordinary Least Squares (OLS) regression results highlight Empathy as the most influential SERVQUAL component, followed by Responsiveness, Reliability, Tangibles, and Assurance. Furthermore, review score emerged as the strongest predictor of customer sentiment. These findings provide actionable insights for service providers aiming to enhance customer satisfaction by optimizing key SERVQUAL dimensions and addressing review score trends.

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