| dc.description.abstract | Hotel managers are more likely to analyze travelers' satisfaction and preferences through online
reviews to enhance their marketing strategy and reach an elevated level of satisfaction. Online
reviews are one large piece of information that contributes an essential value on understanding
customer’s behaviors and preferences of products or services in any industry. It is important to
consider both text reviews and ratings but there are not many researchers taken into account
while evaluating hotel service satisfaction. Hence, to overcome this limitation, this study aims
to apply multi-criteria decision making and machine learning techniques to assess service
quality of hotel features based on both textual-reviews and numerical reviews. Numerical
reviews will be segmented into diverse groups with similar characteristic by K-Means
clustering and the features ranking will be implemented to get the decision support. Next, textbased reviews will be used by topic modelling approach - Latent Dirichlet Allocation to get the
traveler’s preferences as customer preferences. The outcomes obtained from this thesis can be
considered to help hotel managers develop marketing strategy and improve customer
satisfaction. | en_US |
