# A Review of Text Corpus-Based Tourism Big Data Mining
## 4 Applications of Text Corpus-Based Tourism Big Data Mining
### Market Supervision  
The tourism market is the basis for tourism to survive in. Research on the tourism destination market has important theoretical and practical significance for tourism development [50]. The existence of the tourism system depends on the existence of tourist demand, which is always related to aspects of the tourism process such as \"food, accommodation, transport, sightseeing, purchase, entertainment\", and is diverse due to the difference of tourist natural and social attributes. By the analysis of tourist demand and preferences, researchers or practitioners can assess the market composition of tourism destinations and adjust the tourism market resource allocation or make marketing strategies to maximize the degree of satisfaction of tourists. In the context of big data, the online tourism market is gradually driven by user data. Texts, as a main component of user-generated content, can accurately reflect the needs of visitors. From  
Figure 2: Tourist recommendation system framework based on text mining. the perspective of market or tourism stakeholders, this paper summarizes five aspects of text content analysis: Target topic, dimension and weight of concerns, satisfaction evaluation or preference, the reason for sentiment, and new trend, to assist market strategy planning. The analysis of specific \"target topic\" of the text can analyze the tourist needs more specifically; \"dimension and weight of concerns\" is to analyze the tourist demand from their attention to various aspects and compute their weights on the attention; \"satisfaction evaluation or preference\" is to analyze the sentiment orientation of comments to obtain tourists' satisfaction with the travel experience; \"the reason for sentiment\" uses sentiment cause detection techniques to detect the cause of sentiment in order to find the reason behind the sentiment; and the \"new trend\" analyzes the emergence and developing process of new things from the perspective of time series. Next, we will explain with examples in detail. The study [17] explored the key elements of hotel customer comment by the topic model LDA and analyzed the significance of their influence through the perception map of hotel reviews, which are important for the analysis of customer satisfaction. The study [18] combined the three elements of tourist market share, tourist sentiment orientation, and potential tourist awareness to define and calculate the competitiveness of the tourism destination market, the tourists' sentiment orientation of which is obtained through the sentiment analysis model based on text comments. In this study [19], sentiment analysis on a specific topic, \"traffic\", was conducted to analyze the causes of negative sentiments by using the method of co-occurrence of words and evaluation objects. The destination image is a reflection of the tourist market, including the national country image, the city image, the scenic spot image, etc. Research on the destination image was first proposed by [PERSON] [153]. As a basis of market positioning, the destination image has attracted a lot of attention from scientists. Compared with the traditional customer survey method, the method of user-generated text analysis can reflect the various dimensions of the destination image more accurately [154], and the real travel experience of the tourists can effectively improve the accuracy of the destination image evaluation [155]. This research [20] was the early study of using online text content for destination image analysis. With the rapid process of \"Internet Tourism\", more and more researches have begun to explore text analysis as a means of destination image analysis [16; 21]. In the process of evaluating the destination image, it is necessary to recognize the tourists' sentiments and the sentiments for all aspects in their tourism in order to assess the satisfaction of the destination components [21; 22].