# A Review of Text Corpus-Based Tourism Big Data Mining
## 3 Text Corpus-Based Tourism Big Data Mining Techniques
### Text Corpus-Based NLP Techniques in Tourism Data Mining
#### 3.2.4 Text Clustering  
Text clustering mainly involves unsupervised algorithms that can discover potential knowledge and rules from large-scale text data sets, facilitating the effective organization, abstracting, and navigation of texts. In the field of tourism, text clustering is mainly applied in the research of tourist hotspots or emergencies. For example, the study [128] performed co-occurrence clustering analysis by constructing a high-frequency word co-occurrence matrix in order to acquire hot things, and measured the weights of the connection between the regions within or outside the Tibet by degree centrality in the social network map; the tourist hotspots and their interrelationships were then obtained and tourism planning was further promoted. In this study [129], cohesive hierarchical clustering methods were used to detect the emergencies by using bursty topics to represent texts. Besides, text clustering can also be applied in the subdivision problem of the tourism market in which a clustering method is used to obtain different characteristics of the group for targeted analysis and self-improvement. In this study [130], the collaborative clustering algorithm was used to cluster the five-star hotels and hotel reviews in Rome from two dimensions, which not only solved the feature clustering of each hotel, but also solved the description of the features. As mentioned above, text clustering can be an efficient method for tourism analysis. Next we will review it from the technical perspective. The object for text clustering can be documents, sentences, paragraphs, and so on. Similarity is the basis of text clustering. At present, the mainstream text similarity calculation method is mainly based on vector space method, including cosine similarity, Manhattan distance, Euclidean distance, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time** & **Model** & **Basic Idea** & **Accuracy (\%)** \\ \hline
2019 & BERT [125] & Bidirectional Transformer (BERT) is extended with an additional task-specific layer and fine-tuned on each end task & 81.54 \\ \hline
2019 & BERT-PT [125] & On the basis of BERT, two pre-training objectives are used: Masking language model (MLM) and next sentence prediction (NSP), to post-train domain knowledge, and else task (MRC) & 84.95 \\ \hline \hline \end{tabular} The sentiment classification is performed in a three-way classification.  
\end{table} 
Table 5: Common aspect-based sentiment analysis (ABSA) and target-dependent sentiment analysis on subtask 1 (slot 2) SemEval 2016 Task 5 in restaurant domain. and so on. With the development of deep learning, word vectors generated by neural networks such as Word2 vec can make words closer in semantic distance, and are more suitable for similarity calculation of various text granularities. Document clustering techniques include multiple types, such as agglomerative hierarchical clustering algorithm, partitioning clustering algorithm, density-based clustering algorithm, etc. Different clustering algorithms have different requirements for application scenarios, and the prior knowledge of specific tasks [131] must be considered. Currently, there are few researches on text clustering algorithms. The main reason is that the computational overhead of clustering algorithms tends to be large. When the amount of data rises to a certain extent, most clustering algorithms cannot be used, so the time complexity of most clustering algorithms needs to be considered [132]. K-means, which belongs to the partitioning clustering algorithm, is a commonly used text clustering algorithm whose disadvantage is that it cannot effectively determine the number of clusters and select the initial clustering point, and has poor performance on high dimensional data, etc. While compared to other clustering algorithms, K-means is fast and easy to implement on a huge database [36].