<![CDATA[The growth in the retail industry means that the retail industry must have a competitive advantage to compete. One source of competitive advantage is customer experience. One factor that has a positive influence on customer experience is the service provided by frontline employees. Nowadays, customers can easily share their experiences and information in online reviews. Therefore, a good understanding of online reviews is necessary to maintain customer satisfaction. This paper proposes a new method for obtaining information from online reviews available on online review platforms such as Google Maps. Reviews on the website will be scraped and translated into English using the large language model (LLM). The translated reviews will be translated to obtain aspects, sentiments, and opinions using an aspect-based sentiment analysis (ABSA) model that has been previously drilled using a dataset in English. The findings are visualized into Pareto diagrams and word cloud to identify aspects related to human resources that most influence the negative or positive ratings given by customers through online reviews.]]>
