Analyzing and Responding to Google Maps Reviews with a Chatbot in Healthcare

This paper aims to explore how Google Maps reviews for an education and research hospital can be analyzed and responded to using a chatbot. The study highlights the importance of customer feedback in improving hospital services and describes how classification algorithms can be used to collect and analyze reviews. It compares five algorithms to analyze reviews. The chatbot designed in this study responds to reviews and offers personalized suggestions to patients using the most accurate one among five algorithms. The findings suggest that automated chatbot responses can save time and resources while improving the hospital’s online reputation. The study concludes that implementing a chatbot for Google Maps reviews can enhance patient satisfaction and lead to better overall service quality. Among the five classification algorithms used within the scope of the study, it was determined that Naive Bayes and Neural Networks algorithms gave the highest accuracy rate with 79% when categorizing the comments according to the subject and performing the sentiment analysis at the same time. However, other algorithms’ success rates are similar, and the chatbot responds to people by using the results of the algorithm with the highest success rate for each newly entered sentence.