# Targeted Sentiment Classification Based on Attentional Encoding and Graph Convolutional Networks
## 4 Experiments
### Experimental Results
Based on the deep learning model, the performance of the proposed model was better than the traditional machine learning methods. In Table 2, the SVM model proposed by [PERSON] uses an SVM for classification, which relies on a large number of artificial feature extractions. There is no artificial feature extraction in AEGCN-GloVe, and its accuracy was 9.76%, 5.42%, and 0.88% higher than SVM on twitter, lap14, and restaurant datasets, respectively. This shows that the deep learning model is suitable for specific aspect sentiment analysis. It is better to model context semantics by exploiting Bi-LSTM combined with a multi-head attention mechanism rather than by applying only a standard multiple-attention or multi-head attention mechanism. Taking MemNet as an example, it uses multiple hops to combine different attentions linearly, and its accuracy and F1 score were lower than that of AEGCN-GloVe on all five datasets. One possible reason is that once the traditional attention mechanism incorrectly assigns weights to words that are not related to determining the sentiment polarity of a specific target, repeating the traditional attention mechanism multiple times will make it more difficult for the model to predict the correct sentiment polarity of a specific target. In addition, AEN only uses the multi-head attention mechanism to semantically model the contextual and specific targets. Without using Bi-LSTM, it may not be possible to adequately consider the contextual semantics of the entire sentence from front to back and back to front. Except for one index (rest4, F1), AEGCN-GloVe obtained higher values of accuracy and F1 score than AEN in three data sets (twitter, lap14, and rest14). This indicates that the semantic information extracted by combining Bi-LSTM and multi-head self-attention was more abundant. Due to the combination of syntactic information, the effect of this model was better than the model that does not consider it. Although AOA emphasizes the influence between the contexts and the target words through attention over attention, it only achieved slightly higher accuracy and F1 score over rest16 dataset (0.11% higher) and lower values in the other datasets. Moreover, though IAN improves the interaction between the contexts and the target words through the interaction of the interactive attention mechanism, the accuracy and F1 score of the model in five datasets were lower than those for AEGCN-GloVe. TNet-LF could integrate the specific target information into word representation well, and the context-preserving mechanism could retain semantic information well. In the rest16 dataset, the performance of TNet-LF was slightly higher than AEGCN-GloVe (1.7% and 2.21% respectively). However, it was worse than AEGCN-GloVe in the other four datasets. We suppose that the sentences in the rest16 dataset are particularly dependent on the original semantic information of the text, and the application of syntactic information for targeted sentiment classification was successful. The performance of GCN with point-wise convolution was better than the GCN with an aspect-specific masking layer. In the ASGCN model, a multi-layered graph convolution structure is implemented on top of the LSTM output, followed by a masking mechanism that filters out non-aspectwords and keeps only aspect-specific features. However, the contextual representations with syntactic information are lost. We cannot conclude that some context words with syntactic information are useless in determining the emotional polarity of a particular target, so we chose to keep all the context words with syntactic information and encoded them with multi-head self-attention. We believe that after using the syntactic information to reshape the representation of each context word through GCN, the syntactic information-rich words will be more prominent after the parallel calculation of multi-head self-attention, which is more conducive to the full use of syntactic information to determine the sentiment polarity of specific targets.