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A Review of Text Corpus-Based Tourism Big Data Mining

1,2, 3,4,*, 1,2, 5 and 3,6,*
1
Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu 610041, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
School of Mechanical Engineering, Guizhou University, Guiyang 550025, China
4
Guizhou Provincial Key Laboratory of Public Big Data (Guizhou University), Guiyang, Guizhou 550025, China
5
College of Big Data Statistics, GuiZhou University of Finance and Economics, Guiyang, Guizhou 550025, China
6
Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2019, 9(16), 3300; https://doi.org/10.3390/app9163300
Received: 25 June 2019 / Revised: 5 August 2019 / Accepted: 6 August 2019 / Published: 12 August 2019
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PDF [690 KB, uploaded 12 August 2019]
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Abstract

With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists’ opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and deep learning. With the understanding of the complexity due to this diverse set of techniques and tourism text data sources, this work attempts to provide a detailed and up-to-date review of text mining techniques that have been, or have the potential to be, applied to modern tourism big data analysis. We summarize and discuss different text representation strategies, text-based NLP techniques for topic extraction, text classification, sentiment analysis, and text clustering in the context of tourism text mining, and their applications in tourist profiling, destination image analysis, market demand, etc. Our work also provides guidelines for constructing new tourism big data applications and outlines promising research areas in this field for incoming years. View Full-Text
Keywords: tourism big data; text mining; NLP; deep learning tourism big data; text mining; NLP; deep learning
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Li, Q.; Li, S.; Zhang, S.; Hu, J.; Hu, J. A Review of Text Corpus-Based Tourism Big Data Mining. Appl. Sci. 2019, 9, 3300.

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