A Review of Text Corpus-Based Tourism Big Data Mining

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-, 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. Appl. Sci. 2019, 9, 3300 2 of 27 the travel. In order to utilize this user-generated content properly and further to meet the needs of tourists and promote the tourism industry, we need to analyze and exploit tourists' needs and opinions, and then identify the problems of tourism services or destinations, which has become a new path for tourism development. Besides, as tourism needs become increasingly personalized, visitors begin to pursue self-likeness, self-worth, and diversified travel experiences, and they are no longer willing to endure delays or waits. How to recognize and respond to visitors' behaviors and needs quickly and identify potential customers have become essential factors for the success of tourism stakeholders. By exploiting the subjective information contained in tourism text data, we can assist tourism stakeholders to provide better services for tourists.A large number of text mining techniques have been proposed and applied to tourism text data analysis for creating tourist profiles [8][9][10][11][12][13][14][15] and making effective market supervision [16][17][18][19][20][21][22][23][24][25]. These approaches exploit a variety of text representation strategies [26][27][28][29][30][31][32] and use different NLP techniques for topic extraction [33], text classification [34], sentiment analysis [35], and text clustering [36]. Moreover, while aiming to make computers understand human language, NLP has become the essential tool for text data analysis and is undergoing fast-pace growing based on the applications of deep learning in word embedding, syntax analysis, machine translation, and text understanding. Machine learning-based NLP techniques have been widely used in tourism text analysis, with superior results [19,25]. In addition, due to its high capability for extracting selective and invariant features from texts, and its independency of prior knowledge and linguistic resources, deep learning has been reported to achieve higher performance than other approaches on many NL...

Evaluation of tourist reviews on TripAdvisor for the protection of the world heritage sites: Text mining approach

“…Text mining works with a wide range of disciplines, such as statistics, artificial intelligence and natural language processing, to analyze data from natural language texts (Miner et al, 2012). Text mining uses the methods of text classification, text clustering, topic extraction, sentiment analysis or opinion mining by using the disciplines mentioned above (Li et al, 2019).…”

Voice of urban park visitors: exploring destination attributes influencing behavioural intentions through online review mining

“…Text analysis is a sort of natural language processing (NLP) technology. NLP has gone through an array of technologies such as naive Bayes, TF/IDF, word2vec, LDA, LSTM, fast- text, BERT and even the latest ALBERT [21,22], which has vastly improved the text analysis quality and efficiency. The analysis process of recent researches on visitors' behaviours can be classified into two categories.…”

On Ontology-Based Tourist Knowledge Representation and Recommendation

“…After an interview with the user, in their opinion, the advantage of this system is to find the link and idea for each tourist attraction, and to provide the users with new tourists. (5) According to the research by Li et al (2019) [30], text corpus-based tourism helps to observe and realize changes in the tourism market. OTR in this study is viewed as a kind of text corpus based tourism, which of the direction is confirmed to be correct, because the study by Gong, et al (2018) [31] has the same opinion.…”