Analyzing online reviews at the word level to understand customer experience

Ha Thi Thu Nguyen

doi:10.4108/eetcasa.7059

Authors

Ha Thi Thu Nguyen FPT University

DOI:

https://doi.org/10.4108/eetcasa.7059

Keywords:

Customer Experience, Trung Nguyen Legend, Sentiment Analysis, CSAT, NPS, Natural Language Processing

Abstract

INTRODUCTION: In the competitive business environment, customer experience plays a pivotal role in driving brand success. Brands that deliver exceptional customer experiences benefit from increased loyalty, advocacy, and stronger market differentiation. With the rise of digital platforms, customers frequently share post-purchase experiences online, making sentiment analysis essential for strategic marketing.

OBJECTIVES: This study aims to explore customer experience with the Trung Nguyen Legend coffee brand by analyzing user-generated content on TripAdvisor. It seeks to identify key aspects of customer feedback and measure satisfaction and loyalty levels.

METHODS: The research employs natural language processing (NLP) techniques and Python-based sentiment analysis tools. Specifically, aspect-based sentiment analysis (ABSA) is used to extract and evaluate sentiment associated with different service dimensions based on online reviews.

RESULTS: The analysis reveals that Trung Nguyen Legend achieves a Customer Satisfaction (CSAT) score exceeding 66% and a Net Promoter Score (NPS) over 34%. These results indicate a generally positive customer experience, with specific strengths and areas for improvement clearly identified.

CONCLUSION: The study demonstrates that ABSA is a cost-effective and time-efficient method for understanding customer sentiment. The findings offer valuable insights for enhancing customer experience management and inform strategic improvements for the Trung Nguyen Legend brand.

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