A Review of Text Corpus-Based Tourism Big Data Mining
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Digital Marketing and Social Media · Diverse Aspects of Tourism Research · Sentiment Analysis and Opinion Mining
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu 610041, China
University of Chinese Academy of Sciences, Beijing 100049, China
School of Mechanical Engineering, Guizhou University, Guiyang 550025, China
Guizhou Provincial Key Laboratory of Public Big Data (Guizhou University), Guiyang, Guizhou 550025, China
College of Big Data Statistics, GuiZhou University of Finance and Economics, Guiyang, Guizhou 550025, China
Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA
Funding
National Natural Science Foundation of China · Science and Technology Project of Guizhou Province · Collaborative Innovation
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