PARDO Phillip Dean
   Department   Ritsumeikan Asia Pacific University  College of International Management
   Position   Professor
Language English
Publication Date 2016/06
Type Book(Whole book)
Title Artificial Neural Network Modelling - Application of Artificial Neural Network in Social Media Data Analysis: A Case of Lodging Business in
Philadelphia (Chapter 16)
Contribution Type Contributor
Volume, Issue, Page pp.Chapter 16
Author and coauthor Le Quang Thai, William Claster and Phillip Pardo
Details Artificial Neural Network (ANN) is an area of extensive research. The ANN has been shown to have
utility in a wide range of applications. In this chapter, we demonstrate practical applications of ANN in
analyzing social media data in order to gain insight into competitive analysis in the field tourism. We have
leveraged the use of an ANN architecture in creating a Self-Organizing Map (SOM) to cluster all the
textual conversational topics being shared through thousands of management tweets of more than ten upper
class hotels in Philadelphia. By doing so, we are able not only to picture the overall strategies being
practiced by those hotels, but also to indicate the differences in approaching online media among them
through very lucid and informative presentations. We also carry out predictive analysis as an effort to
forecast the occupancy rate of luxury and upper upscale group of hotels in Philadelphia by implementing
Neural Network based time series analysis with Twitter data and Google Trend as overlay data. As a result,
hotel managers can take into account which events in the life of the city will have deepest impact. In short,
with the use of ANN and other complementary tools, it becomes possible for hotel and tourism managers
to monitor the real-time flow of social media data in order to conduct competitive analysis over very short
time frames.
Note: S. Shanmuganathan and S. Samarasinghe (eds.), Artificial Neural Network Modelling, Studies in Computational Intelligence 628, DOI 10.1007/978-3-319-28495-8_16
DOI 10.1007/978-3-319-28495-8_16
ISSN 978-3-319-28493-4