# 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
#### 3.2.1 Topic Extraction  
Topic extraction is a technique for extracting topics or aspects from large-scale text data, which can give the decision-maker some insights and help them identify associated sentiments. In tourism, topic extraction can be used to capture tourism public concerns and track hot events, by which tourists can track travel trends and important events and tourism practitioners can find business opportunities or travel crisis in real time, accordingly, to take measures to guide public opinion or promote related fields. Hot topics are often reviewed and reprinted more frequently. Based on this idea, the study [54] filtered out information with high evaluation and reload times, and introduced time distribution \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time** & **Pre-Trained Model** & **URL** & **Accessed Data** \\ \hline
2013 & Word2 vec & [[https://radimrehurek.com/gensim/models/](https://radimrehurek.com/gensim/models/)]([https://radimrehurek.com/gensim/models/](https://radimrehurek.com/gensim/models/)) & 24 July 2019 \\
2014 & Glove & [[https://nlp.stanford.edu/projects/glove/](https://nlp.stanford.edu/projects/glove/)]([https://nlp.stanford.edu/projects/glove/](https://nlp.stanford.edu/projects/glove/)) & 24 July 2019 \\
2016 & FastText [45] & [[https://fasttext.cc/](https://fasttext.cc/)]([https://fasttext.cc/](https://fasttext.cc/)) & 24 July 2019 \\
2016 & WordRank [46] & [[https://radimrehurek.com/gensim/models/](https://radimrehurek.com/gensim/models/)]([https://radimrehurek.com/gensim/models/](https://radimrehurek.com/gensim/models/)) & 24 July 2019 \\
2017 & CoVe & [[https://github.com/salesforce/cove](https://github.com/salesforce/cove)]([https://github.com/salesforce/cove](https://github.com/salesforce/cove)) & 24 July 2019 \\
2018 & ULMFiT [47] & [[http://nlp.fast.ai/ulmfit](http://nlp.fast.ai/ulmfit)]([http://nlp.fast.ai/ulmfit](http://nlp.fast.ai/ulmfit)) & 24 July 2019 \\
2018 & ELMO & [[https://github.com/allenai/allennlp/blob/master/](https://github.com/allenai/allennlp/blob/master/)]([https://github.com/allenai/allennlp/blob/master/](https://github.com/allenai/allennlp/blob/master/)) & 24 July 2019 \\
2018 & OpenAI-GPT & [[https://openai.com/blog/language-unsupervised/](https://openai.com/blog/language-unsupervised/)]([https://openai.com/blog/language-unsupervised/](https://openai.com/blog/language-unsupervised/)) & 24 July 2019 \\
2018 & BERT & [[https://github.com/google-research/bert](https://github.com/google-research/bert)]([https://github.com/google-research/bert](https://github.com/google-research/bert)) & 24 July 2019 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Pre-trained models for word embeddings. calculation methods into LDA to detect hot topics. In the study [55], Structure Topic Model (STM) [56] was adopted to detect negative reviews and how public concerns vary over different hotel grades. In addition, topic extraction can also be used to find tourism characteristics of attractions or build tourism product and user profiles. For example, the study [4] defined the global topic as \"scenic spot\" to filter noise semantics from tourism corpus, to explore the local topics of \"scenic spot\" by LDA, and then display the distribution of the attraction types or the local topics in the form of attraction maps. In this study [5], a season topic model based on LDA (STLDA) was proposed to explore the topic characteristics of attractions with seasonal features, which was of great significance for mining related topics with seasons and for personalized recommendations. In this study [57], a topic model was utilized to detect explicit interests by interactions between users for the following users' implicit interest profile building. Topic extraction has been studied a lot recently and is an important issue for tourism analysis. There are many text mining techniques for topic extraction. Traditionally, keywords are usually used for expressing the topics of documents, and keyword extraction has played an important role in topic extraction for many years.