Mining “What they talk about” for a Private Healthcare Service Provider

• Main Authors: Lee, Angela Siew Hoong *, Lim, Tong Ming *, Chia, Mark P. C, Ea, Sue Lynn *, Yap, Mun Yee *
• Format: Article
• Published: Society for Science and Education 2017
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.sunway.edu.my/792/; http://doi.org/10.14738/abr.55.3057

In every industry, customer feedback contains opinions with different types of sentiment on services and products provided by companies that they purchased from. Feedback from customers help business operators to understand their customers better in order to improve different aspects of their products and services. This research studies healthcare service consumers’ perception of a private hospital on the quality of food, waiting time, services and customer expenses. This research intends to explore a set of customer’s feedbacks from year 2013 to 2016 to investigate potential new findings and to create new value added improvements to the current process. Text mining technique were used to extract and discover hidden knowledge from the unstructured feedbacks. The techniques are text parsing, filter, topic and cluster as for sentiment analysis, term frequency and weight is used in conjunction with corpus of files of text to investigate the emotional elements of the feedbacks that can be classified into either positive, neutral or negative. The outcomes of this study have highlighted several new findings and supported hypothesis of this research