Exploring Customer Insights Through Online Review Analytics

The rapid expansion of digital platforms has transformed how customers share service experiences, generating large volumes of online feedback that can be used to evaluate service quality in the retail pharmacy sector. This study applied computational linguistic techniques to analyze customer opinions and assess service performance in Vietnam's pharmaceutical retail industry. Using Python-based natural language processing and sentiment analysis, 11,938 customer reviews were collected from the top five drugstore chains and classified into positive, negative, and neutral sentiments. Aspect-based sentiment analysis was then employed to identify key service quality dimensions, including staff professionalism, product availability, pricing, consultation quality, and store environment. Results indicated that customer opinions were mainly positive, highlighting staff friendliness, cleanliness, and reliability, while negative sentiments were often related to waiting times and limited product availability. These findings support data-driven decision-making and service improvements in pharmacy retail.