# 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 c c c} \hline \hline
**Time** & **Model** & **Basic Idea** & **Accuracy (\%)** \\ \hline
2016 & AE-LSTM [113] & The target words given in each sentence of the training corpus are vectorized and added to the LSTM model as input for training together. & 76.20 \\ \hline
2016 & AT-LSTM [113] & An attention mechanism is proposed to capture key parts of a sentence related to a given aspect. & 77.90 \\ \hline
2018 & AF-LSTM [114] & A new association layer that defines two correlation operators, circular convolution and cyclic correlation, is introduced to learn the relationship between sentence words and aspects. & 75.44 \\ \hline
2015 & TD-LST [115] & Two LSTM networks are adopted to model separately, based on context before and after target words for target-dependent sentiment analysis tasks. & 75.63 \\ \hline
2015 & TC-LSTM [115] & On the basis of TD-LSTM, target word information is added as an input. & 76.01 \\ \hline
2016 & ATAE-LSTM [113] & On the basis of TD-LSTM, aspect information is introduced in two parts of the model: Input part and hidden part & 77.20 \\ \hline
2017 & IAN [116] & It learns attentions in target and context words interactively, and generates the representations for targets and contexts separately. & 78.60 \\ \hline
2016 & MemNet(k) [117] & It uses deep memory network with multiple computational layers (hops) to classify sentiments at the aspect level, where k is the number of layers. & (k = 2) 78.61 \\ \hline
2018 & Coattention-LSTM [118] & A collaborative attention mechanism is proposed to alternately use target-level and context-level attention mechanisms. & 78.8 79.7 \\ \hline
2017 & BILSTM-ATT-G [119] & Based on the Vanilla Attention Model, this model is extended to differentiate left and right contexts, and uses the gate method to control the output of the data stream. & 79.73 \\ \hline
2018 & TNet-LF & The CNN is used to replace the attention-based recurrent neural network (RNN) to extract the classification features, and the context-preserving transformation (CPT) structure such as lossless forwarding (LF) and adaptive scaling (AS) is used to capture the target entity information and the retention context information. & 80.79 80.69 \\ \hline
2018 & AE-DLSTMs [121] & On the basis of AE-LSTMs, this model captures contextual semantic information in both forward and backward directions in aspect words. & 79.57 \\ \hline
2018 & AELA-DLSTMs [121] & Based on AE-DLSTMs, this model introduces the context position information weight of the aspect word. & 80.35 \\ \hline
2018 & StageI+StageII [122] & It introduces a position attention mechanism based on position context between aspect and context, and also considers the disturbance of other aspects in the same sentence. & 80.10 \\ \hline
2018 & DMN+AttGRU (k = 3) & A dynamic memory network which uses multiple attention blocks of multiple attention mechanisms is proposed to extract sentiment-related features in memory information, where k stands for attention steps. & 81.41 \\ \hline
2018 & MGAN [124] & This model designs an aspect alignment loss to depict aspect-level interactions among aspects with the same context, and to strengthen the attention differences among aspects with the same context and different sentiment polarities. & 81.25 \\ \hline \hline \end{tabular}  
* In the training process, the pre-trained word vectors in these models were all initialized by 300-dimension Glove embeddings and the sentiment classification was performed in a three-way classification.  
\end{table} 
Table 4: Common aspect-based sentiment analysis (ABSA) and target-dependent sentiment analysis on SemEval 2014 Task 4 in restaurant domain. As shown in Tables 4 and 5, the sentiment classification algorithms are all trained on existing data sets from SemEval2014 and SemEval2016 on which they achieved excellent performance, while the limited data sets making them not universally applicable in other new domains.