Utilizing Google Map Reviews and Sentiment Analysis: Knowing Customer Experience in Coffee Shops
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Electronic word of mouth (eWOM) is a good source of information, and this includes customer reviews. Through this review, consumers make informed decisions. In this study, the researchers utilized Google Maps Reviews of customers of three known coffee shops. A google map review scraper was used to extract all customer's reviews and star ratings. In order to extract important information from reviews, opinion mining was done. MATLAB R2022a was used for sentiment analysis and opinion pre-processing. Each coffee shop's most popular words are represented using the Bigram model and the bag-of-words technique. This allows for the visual identification of the unique characteristics of these coffee businesses. According to the study's findings, coffee shop B had the most positive average percentage sentiment score (73%), while coffee shop C had the least negative average sentiment score. The Bigram model shows that customers enjoy the coffee these three coffee shops serve. However, when it comes to taste, location, bread, and pastries, coffee shop C has the most words. Lastly, the correlation values for star ratings vs sentiment scores for coffee shops A and B are r=0.4726 and r=0.4812. There is absolutely no association between sentiment score and star ratings for coffee shop C.
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