# A Multi-Attention Network for Aspect-Level Sentiment Analysis
## 4 Experiments
### Main Results  
According to the datasets, the performance of MAN and other models are shown in Table 2, which represents the classification accuracy and macro-F1. From Table 2, the accuracy indicates the probability of being divided into three polarities of sentiment {positive, negative, neutral}. We could observe that our proposed MAN model obtains the best performance. Comparing the baseline models with LSTM, ATAE-LSTM and IAN perform better than LSTM because they all make full use of the information about aspect term and attention mechanism. It is obvious that LSTM cannot obtain the target information in the sentence, even if given different targets, when the corresponding sentiment polarity is the same. ATAE-LSTM uses aspect embedding twice and an attention mechanism by giving different weights to different words in the aspect term. IAN strengthens the interaction between aspect term and context which uses connected attention networks. Thus, IAN improves 1.4 points and 3.4 points on restaurant and laptop datasets in accuracy, respectively. For MemNet and RAM, they achieve better results of the three datasets, among which RAM introduces position weight, which is similar to the MAN model. They all take into account the influence of location. At present, there are few methods to solve ASA tasks by applying location encoding, so we can apply it further in the later stage. Although the performance of RAM is better than other methods, it performs less competitively than our MAN model on all datasets. RAM uses Bi-LSTM to learn aspect term and context representations. In addition, RAM employs multiple recurrent attention models to obtain weights in different context words. Compared with the model proposed in this paper, which \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**Restaurant**} & \multicolumn{2}{c}{**Laptop**} & \multicolumn{2}{c}{**Twitter**} \\ \cline{3-8}  & & **Accuracy** & **Macro-F1** & **Accuracy** & **Macro-F1** & **Accuracy** & **Macro-F1** \\ \hline \multirow{4}{*}{**Baseline**} & LSTM & 74.28 & - & 66.45 & - & - & 82.55 \\ \cline{2-8}  & ATAE-LSTM & 77.20 & 90.90 & 68.70 & 87.60 & 66.62 & 85.63 \\ \cline{2-8}  & IAN & 78.60 & - & 72.10 & - & - & - \\ \cline{2-8}  & MemNet & 78.16 & 65.83 & 70.33 & 64.09 & 68.50 & 66.91 \\ \cline{2-8}  & RAM & 80.23 & 70.80 & 74.49 & 71.35 & 69.36 & 67.30 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} MBTN-BERT \\ ablations \\ \end{tabular} } & MAN-BERT w/o & 80.02 & 88.99 & 73.62 & 88.49 & 70.24 & 87.21 \\ \cline{2-8}  & MAN-BERT w/o & 81.54 & 90.69 & 75.90 & 89.92 & 70.11 & 87.68 \\ \cline{2-8}  & IAN-BERT w/o & 80.00 & 85.88 & 71.78 & 87.83 & 69.25 & 86.45 \\ \cline{2-8}  & MAN-BERT-Bi-LSTM & 81.30 & 91.19 & 73.59 & 87.92 & 71.29 & 87.65 \\ \hline Ours & MAN-BERT & **81.43** & **91.22** & **76.35** & **88.13** & **71.35** & **87.92** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison with accuracy and macro-F1. “-” denotes not reported. Best results are in bold. adopts a bidirectional attention structure, and the influence of feature extraction and the analysis context on the aspect term is better than that of RAM. The experimental results also confirm this point. According to the final results of the experiments, we can find that MAN-BERT's results are far better than RAM, and the maximum difference value is more than 20 points. Therefore, the method in this paper is much better than the traditional methods. As shown in Table 2, the performance of MAN-BERT ablations is not as good as MAN-BERT in accuracy and macro-F1, which shows that our proposed model is indispensable in structure and parameter settings. When MHA and PFFN in the MAN model is replaced by Bi-LSTM, the accuracy and macro-F1 of the two models are close because they have the same influence. At present, most sentiment analysis tasks are solved by neural networks. In this paper, from the perspective of replacing neural networks with a transformer, the hidden information is obtained in depth.