# A Review of Text Corpus-Based Tourism Big Data Mining
## 4 Applications of Text Corpus-Based Tourism Big Data Mining
### Tourist Profile
The topic-based model is an unsupervised algorithm which can extract user preferences or hobies, etc., and is an efficient method for the acquisition of the user's attribute information, except for explicit feature classification [11]. Furthermore, the unsupervised aspect-based or target-dependent sentiment analysis, which is studied a lot currently, can recognize user preference for aspects or the target, and provide a more fine-grained analysis for user profiles [12]. Tourist profile is a feature extraction process and a vital step of the personalized recommendation in tourism big data mining. The personalized recommendation system is a process of intelligent recommending for users according to their preferences, habits, and individual needs. In the field of tourism, the recommendation system is more complicated, because we not only need to consider personal attributes, but also need consider travel characteristics. These two considerations jointly determine tourist decision-making behavior [146]. Travel characteristics include destination type, travel distance, traffic mode, travel expenses, and so on. Different tourist personal attributes have different features in the performance of travel characteristics [147], and tourist characteristics directly influence the choice of travel characteristics, such as the choice of destination. Mastering the tourist psychological characteristics in travel planning is the critical procedure for a good personalized recommendation system design, and the text reviews become an important supplement to the data sparsity in the tourism recommendation process. By mining user reviews, user preferences, and travel destination, reputation or features can be gained and introduced to the travel recommendation system for final recommendations [148]. Because the topic model can detect tourists' preferences, frequent behaviors, or new travel trends in an unsupervised manner, it has become a hot research direction for scholars. For example, the study [149] mined the feature information of tourists and locations by the topic model, and used knowledge-based filtering techniques to achieve destination recommendations for tourists by semantic similarity. The study [150] proposed a Topic Criterion (TC) model by improving the topic model and the Topic Sentiment Criterion (TSC) model to calculate tourist profiles and item profiles, as well as their matching degrees to achieve project recommendations for potential tourists. In addition, some scholars have also considered the context of travel in the recommendation process, such as seasons, holidays, etc. In the study [95], a text mining technique was used to calculate the user's sentiment tendency toward the destination, and the influence of time elements such as seasons and holidays on the tourists' sentiments were considered comprehensively to promote the tourism recommendation system greatly. Based on the literature research of tourism recommendation system [151; 152], we summarize the general framework of the tourism recommendation system based on text mining (shown in Figure 2).