Öz
Social media has become an area where people live and share their emotions. Therefore, the comments and evaluation ratings individuals make about the products or services they purchase affect the purchasing behavior of other customers. Customers generally reach an opinion about the emotional tendencies of users through the ratings they give. The fact that businesses predict the emotional tendencies hidden in user comments through user ratings makes the steps to be taken in the marketing process questionable. Sometimes consumers may use positive expressions much more in a low-scoring review of a product and attribute the reason for a low score to a single factor. Similar examples lead to questioning the relationship between ratings and reviews. The research aim of the research is to investigate whether consumers' scores after product and service use can be considered as a measure of emotional tendencies in comments. The research has a quantitative characteristic due to the text mining application for user reviews. Web mining/scraping technique used in the data collection process. The data was obtained from TripAdvisor.com, a popular tourism platform. Sentiment analysis, one of the text mining techniques, was used to analyze the obtained data. R programming language, which has practical use in data mining, was used in the data analysis process. As a result of the research, it was observed that the success of consumer ratings in reflecting positive emotional tendencies is higher. At the same time, there is a gap between negative emotional tendencies.