Voice of customers: Text analysis of hotel customer reviews (cleanliness, overall environment & value for money)

The rise of the Internet has caused many changes on our daily lives. Internet has drastically changed the way we live our life, the way we spend our holidays, how we communicate with each other daily, and how we purchase products. The growth of Internet amongst consumer has generated content on the Internet by sources such as social media, review website, blogs, product fan page and many more. This has lead on to a new way of planning a holiday or looking for a suitable hotel to stay. Hence, hotel reviews websites have become a popular platform for customers to share their experiences, reviews, and recommendations on hotels, which they have visited. In Malaysia, hotel industry has been one of the most important economic growths of the country. The primary goal of a hotel is to satisfy customers, to be able to provide a high quality of service, and provide customers with a memorable experience whilst staying at the hotel. The purpose of this study is to understand and identify the range of factors, which may contribute to the satisfaction of customers as well as through expectations. Data was collected from online review websites such as Trip Advisor. Text analytics are used to analyze the contents collected

Predicting employee health risks using classification ensemble model

Our planet is known as a digital earth, circulating around data. Growth in data is exponential, leading to an elevated interest in Big Data Analytics, to collect, store, process, analyze and visualize unparalleled amount of data. Modern information driven society will continue to be shaped by big data, where there will be potential to extract meaningful insights and hidden patterns impacting businesses in unforeseen measures. Most employers in Malaysia provide medical benefits which includes general medical costs to hospitalization benefits and insurance coverages; with these data and information stored by the HR (Human Resource), leading to a potential to analyze and identify patterns in historical claims - these insights would lead to improved decision making to better understand employee population health and the usage of the premium coverage. In predictive analysis, common techniques applied are Decision Tree and Regression. Therefore, the aim of this research is to propose a conceptual prediction model to better understand the patterns present in the employee healthcare data while predicting if an employee would be at any health risks to understand the population health and the usage of premium coverage provided by the employer. Additionally, to apply an ensemble method called Stacking, where multiple predictive models will be combined to perform a prediction. An ensemble model will present the opportunity to build a more robust and accurate model which could be applied across various industries instead of being industry specific

A framework for predicting employee health risks using Ensemble Model

Through the phenomenon of data, big data and data analytics have provided an opportunity to collect, store, process, analyze and visualize an immense amount of information. Healthcare is recognized as one of the most information-intensive sectors. An urge to explore analytics has been sparked by the rapid growth of data within the healthcare sector. Most employers in Malaysia provide medical benefits that are included in the medical insurance plan for their employees. Data collected such as the history of medical claims are stored with the HR (Human Resource) which contributes to the potential of analyzing and recognizing trends within medical claims to better understand the use and overall health of the employee population. Patients with higher risk will generally convert into patients with high costs. Hence, early intervention of these patients will allow employers to potentially minimize costs and plan preventative steps. In predictive analysis, Decision Trees and Regression are typical techniques applied. The proposed framework combines an ensemble technique known as Stacking. As opposed to a single predictive model, an ensemble predictive model would yield better performance and accuracy. The objective of this paper is therefore to review current practices and past research within the healthcare sector while suggesting a practical framework for classification ensemble modeling. Preliminary findings indicated that an ensemble model can produce higher predictive accuracy and performance than a single model