# A Review of Text Corpus-Based Tourism Big Data Mining
## 5 Outlook  
Tourism big data in the form of text plays an important role in tourism applications. First of all, tourism is a service system, emphasizing the sentiment or value experience of tourism individuals. Text mining techniques have become indispensable to the sentiment judgment and value-oriented analysis in modern tourism applications. Secondly, text mining techniques are experiencing a period of rapid development and are achieving much improvement. Benefit from deep learning techniques such as text classification and sentiment analysis have made many breakthroughs [159]. However, text mining techniques based on deep learning are often less practical in tourism due to the requirements of deep learning for data volume and labeled data, and most of them only use existing data to explore future tourism trends. Aiming at the problem of lack of existing standard tourism corpus and the limitations of deep learning such as interpretability, this paper makes a detailed analysis and puts forward some major trends of future tourism text data mining. (1) Lack of domain corpus. The languages of the existing tourism corpora are mostly English and the limited multi-language categories make the existing tourism corpora not universally adaptable. In addition, the annotation of the tourism corpus often relies on manual labor, lack of system and formativeness, and the scale of the corpus is usually small. How to automatically and effectively construct a standardized large-scale multi-language tourism corpus has become one of the keys to the successful application of tourism big data. Given the impact of publicly annotated data sets on tourism big data mining and for the convenience of research, we summarize some of the relevant publicly available text data sets currently in the tourism domain, with the data sets described and the dataset sources listed in Table 7.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Name** & **Description** & **Source** & **Accessed Date** \\ \hline \multirow{4}{*}{Hotel\_Reviews 515,000} & It contains 515,000 customer reviews with positive and negative aspects and ratings of 1493 luxury hotels across Europe, as well as the location of the hotel. & [[https://www.kaggle.com/jiashenliu/515k-hotel-reviews-data-in-europe](https://www.kaggle.com/jiashenliu/515k-hotel-reviews-data-in-europe)]([https://www.kaggle.com/jiashenliu/515k-hotel-reviews-data-in-europe](https://www.kaggle.com/jiashenliu/515k-hotel-reviews-data-in-europe)) & 24 July 2019 \\ \hline TripAdvisor Hotel Review Dataset & It contains 20,490 hotel customer reviews and related review ratings. & [[https://zenodo.org/record/121989994.XFSETygzY2w](https://zenodo.org/record/121989994.XFSETygzY2w)]([https://zenodo.org/record/121989994.XFSETygzY2w](https://zenodo.org/record/121989994.XFSETygzY2w)) & 24 July 2019 \\ \hline \multirow{4}{*}{Citysearch Restaurant Review Dataset} & It contains 35,000 food reviews and lists representative words related to the attributes of each entity. It also includes 3418 sentences with labeled sentiment polarity for the attributes of each entity. & [[http://dilab.korea.ac.kr/jmts/jmtsdataset.zip](http://dilab.korea.ac.kr/jmts/jmtsdataset.zip)]([http://dilab.korea.ac.kr/jmts/jmtsdataset.zip](http://dilab.korea.ac.kr/jmts/jmtsdataset.zip)) & 24 July 2019 \\ \hline \multirow{4}{*}{OpinRank Dataset} & It contains 259,000 reviews of 10 different cities (Dubai, Beijing, London, New York, New Delhi, San Francisco, Shanghai, Montreal, Las Vegas, and Chicago), each city of which has approximately 80–700 hotels, with dates, comment title, and full comment included.