# 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
#### 3.2.2 Text Classification  
Text classification is a process in which the computer automatically classifies the input texts according to their content, and it includes spam filtering, information retrieval, topic classification, sentiment classification, and so on; sentiment classification of which will be discussed in the Section 3.2.3. In tourism, text classification is generally about topic classification, and the topics of texts in travel mostly involve various aspects of the travel process, such as transportation, accommodation, food, entertainment, and so on. In addition, tourism texts such as tourism comment reviews are often short but contain a lot of information. They usually contain multiple topics but cannot be attributed to one certain aspect or target. Therefore, text classification in the current tourism field is usually carried out by topic extraction, to extract all aspects of the text for targeted analysis [22; 75].  
Traditionally, text classifications are mainly based on machine learning such as Naive Bayes [76], maximum entropy [77], Support Vector Machine [78], K-nearest neighbor algorithm [78], etc. They usually use keywords or topics to reflect the feature of documents and realize text classification automatically [79], which still plays an important role currently [80]. At present, the mainstream feature extraction techniques characterized by keywords include TF-IDF and information divergence, and more advanced deep learning approaches [81]. Besides, there are some classic feature representation models for words such as Vector Space Model, N-grams [82], etc., but these models have some disadvantages, such as semantic information being missing. With word-distributed representations, text classification no longer relies on the keywords only and begins to pay more attention to the semantics of words themselves. Following this trend, there have emerged a lot of text classification algorithms based on the improvement of word embedding features [45; 65; 83].  
Benefitting from deep learning, various researches on text classification techniques based on deep neural networks have also made significant progress. For example, the representative and innovative algorithms of CNN in text classification include the CNN proposed by [PERSON] [34], character-level convolutional network (ConvNets) proposed by [PERSON] [84], the CNN-based classification algorithm applied to patent classification [85], the RNN-based text classification algorithms which includes bidirectional RNNs [86], hierarchical attention mechanism (HAN) [35], and the recurrent convolutional network (RCNN) [87]. In addition, the connection between CNNs and RNNs [88], the introduction of multi-task learning frameworks [89], and the increased depth in deep learning models [90] will also improve the performance of text classification to a certain extent.  
At present, text classification models are mostly based on supervised learning. Supervised text classification often relies on the integrity of the domain corpus, as well as its annotations, so researchers are more concerned with the improvement of theory but lack of practical applications. In order to solve the issue of few in-domain labeled data, many following studies have been made; Table 3 shows four different strategies: Co-training, training samples extension, meta-learning, and transfer learning. Among them, co-training and training samples extension need auxiliary training with unlabeled data or external knowledge, but as the training samples increase, the noise in the auto-tagging instance will continue to accumulate. Meta-learning or transfer learning attempt to learn general representation or meta knowledge among tasks, but still have to make further improvement, such as in the issue of negative transfer.