Public Hospital Review on Map Service with Part of Speech Tagging and Biterm Topic Modeling

Abstract

Health is a basic human need. Increasing standard of life influences the quality of health. It demands health service providers improve quality better service and provide satisfaction for consumers as health care users. Hospital as one of the means of health services is required to improve the quality of services. The study found that customers tend to choose private hospitals over public hospitals because of service factors. Public hospitals are gradually being abandoned. This is challenging for public hospitals to improve services. One of the scientific techniques to know the level of public satisfaction with health services, especially hospitals, is to examine the reviews from users. In this research, we use public mapping review as a data source. From the review, it can be known what topics are mostly discussed specifically for various ratings. A suitable model is required to find out the topics in the review that have a low rating so that it can be used as a suggestion for improving health services. Topic modeling is best achieved through the text mining method. The study proposed the use biterm topic model suitable for short text in the reviews of the map platform. Short text reviews are characterized by sparse data, the small number of words that appear as topic builders, and rare word contexts related to the topic. The result shows that biterm topic model can produce topics with an acceptable combination of accuracy and performance. The model with the addition of part-of-speech tagging on noun-only is unable to increase the accuracy of the model, compared to the one without the addition of part-of-speech tag. However, the addition of part-of-speech tagging on noun-only can improve the performance of the model.