# Graph Convolutional Networks with Bidirectional Attention for Aspect-Based Sentiment Classification
## 4 Experiments
### Model for Comparison
#### 4.3.2 Neural Network Models  
TD-LSTM constructs aspect-specific representations by using the upper left and upper right context of an aspect, and then uses the two LSTMS to model the target for the upper left and upper right context, respectively. In order to predict the affective polarity of aspect, the related representations of the left and right targets are connected.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Positive**} & \multicolumn{2}{c}{**Neural**} & \multicolumn{2}{c}{**Negative**} \\ \cline{2-7}  & **Train** & **Test** & **Train** & **Test** & **Train** & **Test** \\ \hline
**Twitter** & 1561 & 173 & 3127 & 346 & 1560 & 173 \\
**Restaurant** & 2164 & 728 & 637 & 196 & 807 & 196 \\
**Laptop** & 994 & 341 & 464 & 169 & 870 & 128 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The details of the experimental datasets.  
ATAE-LSTM proposes an attention-based LSTM model that takes the aspect representation and the word embedded as input, and calculates the attention weights using the hidden state of the LSTM. The attention is used to get sentence representation, and finally the aspect affective polarity is classified. Not only does it solve the problem of inconsistency between word vectors and aspect embeddings, but it also captures the most important information that corresponds to a given aspect.  
MemNet: each word in the sentence is converted into a word vector for representation, and then the weighted sum of the word vectors is used to obtain the context vector. Since the attention mechanism does not have the ability to model the location information, the location embedded in the model is introduced to encode the location information.  
IAN designs the interactive relation between modeling and context model, the model with the context and the target of LSTM network, respectively, based on attention generated terminology and context, said the average pool and pay attention to the mechanism analysis of the weight of different words to find out the important information of the context and specific goal, the final context and aspect representation are linked to predict the sentiment polarity of the aspect.  
AEN models context and aspect separately. AEN uses a submodule similar to Transform to obtain hidden state. During the training process, the model adopts a unique label smoothing regularization method in the loss function. AEN is to model the context and aspects separately, and the submodule similar to transform is used to obtain the hidden state, and the model employs the label smoothing regularization method in the loss function.  
AS-GCN proposes an aspect-oriented graph convolutional network, which uses BL-LSTM to obtain the context information of words, and then multi-layered graph convolutional network to capture syntactic information and long distance dependencies, after which only aspect-oriented information characteristics are retained, and the generated representation is used to predict the emotional polarity based on aspects.  
Bert-pair-QA-M transforms aspect-based sentiment classification into sentence pair classification task, and fine-tuned BERT's pretraining model [41].