# A Review of Text Corpus-Based Tourism Big Data Mining
## 4 Applications of Text Corpus-Based Tourism Big Data Mining
### Tourist Profile  
Tourist profile is used to abstract the specific labels from the attribute information of a tourist. The attributes usually include: Demographic characteristics (individual or organization, age, gender, location), mental state and lifestyle (education, profession, purchasing ability, family, property, emotional attitude, interest, fear, etc. ), travel preferences, and travel purposes. The tourist attributes are diverse, which leads to that the demand is also diverse, driven by the consumption upgrade [142]. Therefore, tourist market segmentation is of great significance to tourism destinations such as the discovery of market opportunities, the planning of right marketing and competition strategies, and the realization of personalized recommendations. By dividing their natural attributes, tourists can be divided into female and male groups, youth and old age groups, single and married groups, local and foreign groups, etc. By the analysis of preferences and behaviors, tourists can be divided into more groups such as travel buyers, the decompressions, and so on. Different groups have different characteristics, and individuals with the same attributes may have similarities in tourism behavior [143]. Study [73] has confirmed that differences in tourist attributes can also lead to differences in tourist perceptions. Through the study of the Cape Town tourism market [144], it was found that visitors' age, place of residence, destination stay time, return visits, etc., had an important influence on the perception of tourists, and the sentiments they conveyed [145] also had different characteristics. Tourist profile are an important means of understanding tourist behavior and meeting the tourist expectation. Since the content of the tourists' comments often reflects their subjective thinking, we can extract information such as preferences, concerns, and purposes of different tourists from the texts. By obtaining their relevant attributes, tourists' profiles can be effectively created. While how to generate user profiles through practical text analysis is still a hot and challenging issue for scholars, through literature research, it has been found that user profiles are mainly obtained by supervised learning, or realized by the feature recognition from data labeled gender, age, occupation, ratings, and so on [14]. In addition, the user attributes for profiles are always treated as isolated in feature recognition; in other words, the relationship between user attributes is ignored, while the attributes are often interrelated. Aiming at this problem, multiple attributes joint learning can be efficient to improve the user attribute prediction [8]. However, although the supervised learning for tourist profiles has achieved good results, it still has limitations because its performance depends entirely on the number of data and its domain. Taking some sample data as the research object, the study [9] extracted the \"co-words\" from different users in the sample to obtain a universal judgment criterion for each user, but the viewpoints or conclusions derived from the sample were often one-sided due to the limited numbers. By using the text information from a large number of existing users on social media, a unified user vector learning model can be obtained to fill the knowledge gap between the source social media and the target social media, and then the problem of uncertainty of user labels for the target media can be solved [10]. Similar works [15] were also done, which considered matching user accounts on different social networks to build user profiles by user identification based on User Generated Content (UGC) in a supervised manner. These methods are all based on this assumption that the data for the same attribute or the same person has common features, such as commonality of the same gender [13], to resolve the problem of the limited labeled data. In supervised learning, current methods for tourist profiles are usually around gender, age, and other explicit feature predictions.