Application of Rule-based Sentiment Analysis and Machine Learning For Sentiment Analysis of Restaurant Reviews in Cappadocia, Türkiye

It is crucial to understand customers' sentiments and opinions about restaurants for both restaurant owners and academics in order to increase customer satisfaction and profitability. The aim of this study was to investigate customer sentiments of restaurants in Cappadocia, Türkiye. 38380 customer reviews of 386 restaurants in Cappadocia were obtained from Tripadvisor. Two different methods were used: rule-based sentiment analysis (RBSA) and machine learning (ML). The topics extracted from the reviews by RBSA were food, place, service, price, view, and staff and the percentages of these topics in the reviews were 41.45%, 23.94%, 11.36%, 9.23%, 8.18%, and 5.84%, respectively. For each topic, sentiment analysis was performed with ML to determine the proportion of positive, negative, and neutral sentiments. The highest positive sentiment content was found in food (40.15%), followed by staff (35.07%) and view (33.78%). Price (4.11%) and service (3.90%) were found to have the highest negative sentiment rates. The percentage of positive sentiment in reviews in Western languages was usually higher than in Far Eastern languages. Combining RBSA and ML techniques can enable both grammatical rules and artificial intelligence techniques while producing appropriate results. By understanding these sentiment patterns, restaurant owners can identify areas for improvement, while researchers can gain valuable insights into consumer behavior and sentiment analysis techniques.