# A Review of Text Corpus-Based Tourism Big Data Mining
## 1 Introduction  
Text is an effective and widely existing form of opinion expression and evaluation by users, as shown by the large number of online review comments over tourism sites, hotels, and services. As a direct expression of users' needs and emotions, text-based tourism data mining has the potential to transform the tourism industry. Indeed, tourists' decision-making is dramatically influenced by the travel experience of other individuals [1] in forms of tourism reviews or blogs, etc. These texts can give valuable insight for potential tourists, and assist them in optimizing destination choices and exploring travel routes, or for tourism practitioners to improve their services. Tourism platforms such as TripAdvisor and Ctrip now routinely provide an explosive amount of text data, which makes it possible to use deep learning [2], NLP [3], and other machine learning [4; 5] and data mining techniques for tourism analysis. Studies [6; 7] have shown that the competitiveness of the tourism industry dramatically relies on tourists' sentiment and opinions about the events occurring duringthe 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 NLP tasks [32]. A range of deep learning algorithms such as deep neural networks (DNNs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs), along with special DNN techniques such as memory strategies and attention mechanisms [37], have been successfully applied to NLP tasks. Due to the significance of tourism text data mining and the emergence of a large number of recent applied text mining techniques with diverse design strategies and methodologies, this paper aims to give a systematic review of these tourism text data mining techniques and applications. This paper divides NLP techniques into two types, in which NLP based on language scenes requires knowledge rather than text, such as knowledge of the domains and common sense, while text corpus-based big data analysis requires only an amount of text data for macro analysis. Figure 1 shows the basic process of applying text corpus-based NLP techniques to tourism analysis.