# 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
According to existing studies, most of keyword extraction techniques are based on unsupervised learning [58], such as algorithms based on statistical word features (e.g., TF-IDF, Kullback-Leibler divergence, chi-square test, etc. ), algorithms based on topic models (e.g., LDA, etc. ), and algorithms based on network graphs (e.g., TextRank, Rapid Automatic Keyword Extraction (RAKE) [59], TopicRank [60]). With the appearance of word-distributed representations, some works have also tried to make use of Word2 vec to improve keyword extraction, while only considering the similarity calculation of words [61]. Keywords can express topics, but currently, their relevance with document topics mainly depends on the improvement of existing probability topic models [33], such as the LDA model. As a probability generation model, LDA can be easily extended to other probability models. For example, the study [62] considered that different topics in the same corpus are often related, and used this idea as a research perspective to promote topic extraction of the LDA model; some probability topic models for specific tasks such as topic sentiments, time changes, document authors, etc. are also expected to extract the topics related to target task [63]. To extract more relevant topics, some experts have also considered using word-distributed representation to improve the semantics and syntactics of text information. For example, the study [64] introduced the similarity of word embedding into LDA to calculate the relevance of the acquired words; the study [65] improved the way of context acquisition by introducing LDA into Word2 vec; the study [66] introduced Word2 vec to calculate the distance between the topic vector and the document vector to correct the topics of LDA mining. Moreover, the combination of deep learning and LDA has become another topic extraction method. For example, in this study [67], a novel neural topic model was proposed to acquire the N-gram topic by the deep neural network and then used LDA to obtain the topic representation of the document; the study [68] integrated LDA into language model LSTM for joint training. In addition to the LDA and its extensions, there are also methods for generating subject-related vectors by the neural attention model, such as Attention-based Aspect Extraction (ABAE) [69]. In summary, researches about topic extraction are mostly dependent on the topic probability model such as LDA, and improved aiming at different text structures. Topic extraction in short text often suffers from data sparseness due to the insufficient word co-occurrence and lack of context information. Using long text to assist short text by importing external-related information from Wikipedia and WordNet, etc. [70], or aggregating short text based on the posterior probability of words in original documents [71] can help with the short text task to some extent. Another approach of improving short text task is to enhance the interpretability or the semantical coherence of the topic model, such as informative words rewarding [72]. Besides, as above analysis, LDA ignores the order structure of texts and the meaning of words, so it is one of the research directions that scholars have focused on by exploring the features of words and sentences, etc., to enhance the ability of topic extraction, and will be show great potential in the future. Except for the issue of short text, in business application, users or practitioners may concern different aspects of a topic or aspect related information; the context information of the topic aspect is often used to explore topic-sensitive content [73]. In order to understand a topic more granularly, the structural relation among topics is also a problem, which is widely concerned such as the exploring the hierarchical structure among topics or the global and local relation [74].