Award Date

May 2017

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Hotel Administration

First Committee Member

Ashok Singh

Second Committee Member

Seyhmus Baloglu

Third Committee Member

Rohan Dalpatadu

Fourth Committee Member

Toni Repetti

Number of Pages

98

Abstract

The rapid growth of technology has changed the dynamics in which consumers socialize and make their purchasing decisions. The volume of online reviews has grown rapidly over the past decade, leading the peer groups of consumer to carry a disproportionate weight in the purchasing decision process. The sheer volume of reviews can be a daunting task for an operator to attempt to incorporate the reviews in their analysis. Sentiment analysis allows for large volumes of consumer reviews to be processed in a relatively easy, and time sensitive manner. The information contained in these reviews, the sentiment score, is the same feeling hospitality consumers are gathering from other consumers prior to making their purchasing decision. To demonstrate the importance of these reviews, this study will seek to model the directional change of a company’s stock price using the sentiment of the consumer’s reviews as the primary predictor. Support Vector Machines will help to classify a year’s worth of consumer reviews on nine distinct properties of a publicly traded Las Vegas gaming/hotel company. This is then modeled using ARIMA modelling techniques to forecast an out-of-time sample, and the accuracy will be assessed by showing that the results being due to random change are minimal. The model is able to accurately predict 28 out of 39 time periods in the out of time sample, which has less than a .0047 probability of being due to random chance.

Keywords

ARIMA; natural language processing; reviews; Sentiment Analysis; text analytics; timeseries

Disciplines

Business Administration, Management, and Operations | Marketing | Statistics and Probability

Language

English


Share

COinS