# 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
\begin{table}
\begin{tabular}{c l c} \hline \hline
**Author-Study** & **Contribution** & **Basic Language Model/Classifier** \\ \hline \multirow{4}{*}{[91]} & It proposes a novel co-training algorithm which uses an & \\  & ensemble of classifiers created in multiple training & [PERSON]; Support Vector \\  & iterations, with labeled data and unlabeled data trained & Machine \\  & jointly and with no added computational complexity. & \\ \hline \multirow{2}{*}{[92]} & It uses the knowledge of Wikipedia to extend the training & [PERSON]; Support Vector \\  & samples, which is realized by network graph construction. & Machine; Random Forest \\ \hline \multirow{4}{*}{[93]} & It introduces an attentive meta-learning method for & \\  & task-agnostic representation and realizes fast adaption in & \\  & different tasks, thus having the ability of learning shared & \\  & representation across tasks. & \\ \hline \multirow{4}{*}{[47]} & It proposes a transfer learning method of universal & \\  & language model fine-tuning (ULMFiT), which trains on & Averaged stochastic gradient \\ \cline{1-1}  & three common text classification tasks; it can prevent & descent Weight-Dropped LSTM \\ \cline{1-1}  & overfitting, even with few labeled data in classification & (AVD-LSTM) [94] \\ \cline{1-1}  & tasks by novel fine-tuning techniques. & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Strategies of few in-domain labeled data in 2018.  
feature of document global semantic information. Aiming at the respective characteristics of the classifiers, such as the dependence of the CNN model on window size and step size [105], and the long-distance dependence problem of the mechanism of the RNN model, the study [106] proposed to use LSTM as the pooling layer in CNN to promote sentiment classification. In view of the fact that there are similar contexts in the representation of sentiment words and the opposite of the emotional tendency of words, such as \"good\" and \"bad\", the research [107] proposed to introduce sentiment information into the word vector, thus promoting the learning and classification of sentiment words. In recent years, the attention mechanism has become one of the mainstream techniques of text processing due to its superior performance. In previous text research, the attention mechanism was mainly applied in the recurrent network structure. For example, in this study [35], a hierarchical attention mechanism (HAN) was proposed to achieve sentiment classification, which constructed the sentence representation with the word-level attention mechanism and the document representation with sentence-level attention mechanism. However, the recurrent network is a sequence-dependent structure, which has a disadvantage in training speed and memory consumption. Aiming at these problems, the Transformer model [37] was proposed, which consists entirely of attention mechanisms and applied self-attention mechanism, and has a complete advantage over the structure of recurrent and convolutional sentiment classification [108]. In the last few years, most of the research on sentiment classification focuses on how to improve the classification accuracy of the entire text, but rarely analyzes the sentiment polarity based on the aspects or targets appearing in the text. In tourism analysis, not only is knowing the overall sentiment tendency of the tourists' comments needed, but also knowing the various sentiments of each entity in the tourism or each aspect of the tourist comments is required, so as to better self-evaluate and propose more targeted solutions. Due to the complexity of the process and the lack of related corpora, most of the works are unable to achieve an effective evaluation of aspect extraction and sentiment classification. The research [3] considered the possibility of describing the topic words by considering the distance between the topic words and the sentiment words, and exploring the preferences of tourists for tourism products.