# 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
#### 3.2.3 Sentiment Analysis  
In tourism, the application of sentiment classification techniques can help manage obtain tourist sentiment tendency and opinions in real time, thus making appropriate measures. For example, the study [95] proposed a tourist destination recommendation system by analyzing and evaluating the user's sentiment tendency; the study [96] explored the sustainable tourism development path through the sentiment analysis of the user reviews of the shared bicycle system in Spain; and in this study [97], a visual analysis system was designed to analyze regional trends and sentiment changes in visitors. Text sentiment analysis is the process of automatically classifying the polarity of a given text with subjective sentiments by computer. It includes many tasks, such as sentiment classification, opinion extraction, and so on [98]. We only discuss sentiment classification here. The method of sentiment classification can be divided into two categories: The first one is based on machine learning methods such as neural networks, and the other is based on dictionary-based methods that use pre-defined sentiment dictionaries such as WordNet, HowNet, LIWC, etc., which have sentiment-related terms, and their polarity values. Dictionary-based methods are based on grammatical rules of text analysis, relying on the quality of the sentiment dictionary and its continuous updating, involving more work refinement, such as the extraction and discrimination of evaluation words, and the consideration of the influence of the word contexts, etc. [99]. In addition to the two types of sentiment classification methods, sentiment classification through the integration of machine learning and dictionary-based methods also shows great potential. For example, this research [100] used the sentiment polarity and part of speech in the sentiment dictionary to extract the feature of the text representation combined with convolutional neural networks. Sentiment classification with machine learning methods is mostly based on supervised learning and relies on the completeness of the labeled training corpus, which is a classification method about features. This research [101] pointed out that feature extraction, feature weight, and sentiment classifier are three essential design elements that affect the accuracy of text sentiment classification. Based on this, sentiment classification with machine learning is mainly carried out around these elements. No matter what kind of design element, the optimal vector representation is sought to achieve better precision and speed for model training. With the development of deep language models and their superiority, sentiment classifiers are more improved and optimized based on recurrent neural networks and convolutional neural networks [102]. Researchers have proposed different feature improvement strategies. For example, for short texts presented on social networks, the study [103] used a two-layer convolutional network to jointly train characters, words, and sentence features. For the long-distance dependence problem of long text, in the study [104], a TopicRNN model was proposed to get the global semantic information, which introduced the topic model to RNN model training to obtain unsupervised