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Big Data and Innovation in Tourism, Travel, and Hospitality

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Cover of 'Big Data and Innovation in Tourism, Travel, and Hospitality'

Table of Contents

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    Book Overview
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    Chapter 1 Composite Indicators for Measuring the Online Search Interest by a Tourist Destination
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    Chapter 2 Developing Smart Tourism Destinations with the Internet of Things
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    Chapter 3 Big Data in Online Travel Agencies and Its Application Through Electronic Devices
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    Chapter 4 Big Data for Measuring the Impact of Tourism Economic Development Programmes: A Process and Quality Criteria Framework for Using Big Data
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    Chapter 5 Research on Big Data, VGI, and the Tourism and Hospitality Sector: Concepts, Methods, and Geographies
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    Chapter 6 Sentiment Analysis for Tourism
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    Chapter 7 Location-Based Social Network Data for Tourism Destinations
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    Chapter 8 Identifying Innovative Idea Proposals with Topic Models—A Case Study from SPA Tourism
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    Chapter 9 Customer Data and Crisis Monitoring in Flanders and Brussels
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    Chapter 10 Analyzing Airbnb Customer Experience Feedback Using Text Mining
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    Chapter 11 Big Data as a Game Changer: How Does It Shape Business Intelligence Within a Tourism and Hospitality Industry Context?
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    Chapter 12 Strengthening Relational Ties and Building Loyalty Through Relational Innovation and Technology: Evidence from Spanish Hotel Guests
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    Chapter 13 Big Data and Its Supporting Elements: Implications for Tourism and Hospitality Marketing
Attention for Chapter 8: Identifying Innovative Idea Proposals with Topic Models—A Case Study from SPA Tourism
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Citations

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Readers on

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21 Mendeley
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Chapter title
Identifying Innovative Idea Proposals with Topic Models—A Case Study from SPA Tourism
Chapter number 8
Book title
Big Data and Innovation in Tourism, Travel, and Hospitality
Published by
Springer, Singapore, January 2019
DOI 10.1007/978-981-13-6339-9_8
Book ISBNs
978-9-81-136338-2, 978-9-81-136339-9
Authors

Gabriele Sottocornola, Fabio Stella, Panagiotis Symeonidis, Markus Zanker, Ines Krajger, Rita Faullant, Erich Schwarz, Sottocornola, Gabriele, Stella, Fabio, Symeonidis, Panagiotis, Zanker, Markus, Krajger, Ines, Faullant, Rita, Schwarz, Erich

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 21 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 19%
Professor > Associate Professor 3 14%
Professor 3 14%
Student > Doctoral Student 2 10%
Researcher 2 10%
Other 1 5%
Unknown 6 29%
Readers by discipline Count As %
Business, Management and Accounting 6 29%
Computer Science 5 24%
Mathematics 1 5%
Social Sciences 1 5%
Design 1 5%
Other 0 0%
Unknown 7 33%