Analyzing and Responding to Google Maps Reviews with a Chatbot in Healthcare
Küçük Resim Yok
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Science and Business Media Deutschland GmbH
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Açıklama
Intelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference -- 22 August 2023 through 24 August 2023 -- -- 299549
Anahtar Kelimeler
Chatbot; Google maps reviews; Healthcare; Sentiment analysis; Text mining, Health care; Hospitals; Chatbots; Classification algorithm; Customer feedback; Education and researches; Google map review; Google maps; Healthcare; Hospital service; Sentiment analysis; Text-mining; Sentiment analysis
