# Targeted Sentiment Classification Based on Attentional Encoding and Graph Convolutional Networks
## 4 Experiments
### Experimental Results  
Eight baseline models are selected for comparison to evaluate the effectiveness of AEGCN, and the comparison of experimental results is shown in Table 2.  
**SVM**[39] is a traditional support vector machine method based on complex feature engineering. **LSTM**[8] gets the hidden layer output of sentences by LSTM, then obtains the sentiment analysis by \(softmax\) classifier. **MemNet**[6] regards the contexts as external memory, which makes the model benefit from a multi-hop architecture. **AOA-LSTM**[40] obtains the hidden layer output of the contexts and target words through Bi-LSTM, then obtains the corresponding representation of the contexts and target words through the interactive learning of attention over attention, and finally acquires the polarity distribution of sentiment by a \(softmax\) classifier. **IAN**[10] acquires the hidden output of context and target words through LSTM, and then obtains the expression of the context and target words through the interactive learning of the interactive attention mechanism. It models the relationships between the target words and their contexts \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**TWITTER**} & \multicolumn{2}{c}{**LAP14**} & \multicolumn{2}{c}{**REST14**} & \multicolumn{2}{c}{**REST15**} & \multicolumn{2}{c}{**REST16**} \\ \cline{2-11}  & **Accuracy** & **F1** & **Accuracy** & **F1** & **Accuracy** & **F1** & **Accuracy** & **F1** & **Accuracy** & **F1** \\ \hline SVM & 63.40 & 63.30 & 70.49 & N/A & 80.16 & N/A & N/A & N/A & N/A & N/A \\ LSTM & 69.56 & 67.70 & 69.28 & 63.09 & 78.13 & 67.47 & 77.37 & 55.17 & 86.80 & 63.88 \\ MemNet & 71.48 & 69.90 & 70.64 & 65.17 & 79.61 & 69.64 & 77.31 & 58.28 & 85.44 & 65.99 \\ AOA & 72.30 & 70.20 & 72.62 & 67.52 & 79.97 & 70.42 & 78.17 & 57.02 & 87.50 & 66.21 \\ IAN & 72.50 & 70.81 & 72.05 & 67.38 & 79.26 & 70.09 & 78.54 & 52.65 & 84.74 & 55.21 \\ TNet-LF & 72.98 & 71.43 & 74.61 & 70.14 & 80.42 & 71.03 & 78.47 & 59.47 & **89.07** & **70.43** \\ AEN & 72.83 & 69.81 & 73.51 & 69.04 & 80.98 & 72.14 & N/A & N/A & N/A & N/A \\ ASGCN-DT & 71.53 & 69.68 & 74.14 & 69.24 & 80.86 & **72.19** & 79.34 & 60.78 & 88.69 & 66.64 \\ ASGCN-DG & 72.15 & 70.40 & 75.55 & 71.05 & 80.77 & 72.02 & 79.89 & **61.89** & 88.99 & 67.48 \\ \hline AEGCN-GloVe & **73.16** & **71.82** & **75.91** & **71.63** & **81.04** & 71.32 & **79.95** & 60.87 & 87.39 & 68.22 \\ AEGCN-BERT & **75.04** & **73.68** & **78.73** & **74.22** & **82.58** & **73.40** & **82.71** & **69.00** & **89.61** & **73.93** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results (%). We use “N/A” to represent unreported experimental results. The top two scores are in bold. Figure 7: Impact of \(R\) over the lap14 dataset. interactively. After splicing the expression of context and target words, the polarity distribution of sentiment is received by a \(softmax\) classifier. **TNet-LF**[12] proposes a method to generate target-specific representations of words in the sentence, incorporating a mechanism for preserving the original contextual information from the RNN layer. **AEN**[22] eschews recurrence and uses an attentional encoder network to model the relation between the contexts and the specific targets. **ASGCN**[15] puts forward a graph convolution network (GCN) on the dependency tree of sentences to take advantage of syntactic information and word dependency. From the experimental results in Table 2, we can see that the AEGCN-GloVe model was slightly better than all the other models in the twitter and lap14 datasets. Compared with the baseline model ASGCN-DT, it obtained comparable results in the rest14 dataset. Compared with the baseline model ASGCN-DG, it still obtained comparable results on the rest15 dataset. However, in the rest16 dataset, the results were slightly inferior to those of the baseline model TNet-LF. In particular, AEGCN-BERT obtained new state-of-the-art performances on all datasets.