Hybrid Sentiment Analysis Model for Customer Feedback Interpretation Using Lexicon, Machine Learning and Deep Learning Techniques
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Abstract
Customer feedback is pivotal in enhancing service quality and user satisfaction across digital platforms. However, traditional sentiment analysis methods often struggle with informal languages, contextual nuances, and aspect-specific opinions. In this paper, a hybrid sentiment analysis framework is proposed, utilizing lexicon-based (VADER), machine learning (Support Vector Machine and Random Forest), and deep learning (BERT) techniques to achieve improved sentiment classification accuracy and interpretability compared to previous studies. The framework incorporates advanced preprocessing techniques, such as emoji normalization, handling of negation, and detection of intensifiers, to better capture emotional information in user-generated content. The objectives of this study are to develop a robust sentiment analysis system that can accurately classify user sentiment and extract aspect-specific insights from customer feedback. Aspect-based sentiment analysis (ABSA) was also employed to provide detailed evaluations of specific service components, including driver behaviour, app performance, and pricing. In this study, experimental results using the Uber Customer Reviews Dataset (2024) demonstrate that the proposed hybrid model achieves 99% accuracy, significantly outperforms the individual model, and obtains a macro F1-score of 0.98. These findings confirm that integrating lexicon-based, machine learning, and deep learning approaches enhances sentiment classification effectiveness and supports data-driven decision making based on user experience.
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