# Aspect-Based Sentiment Analysis Using Aspect Map
## 5 Experiments
### Experimental Results  
The following four models are used as baselines to validate the proposed model. * **UWB**[39]: This is the best model in SemEval 2016 Task 5 for both text-level restaurant and laptop datasets. It uses a large number of hand-crafted features. After representing all sentences in a dataset with a set of hand-crafted features, it classifies the sentiment of every sentence by the maximum entropy classifier. Then, the sentiment of a text is determined by the majority vote of sentence sentiments. Because the datasets for SemEval 2016 are small, top-ranked models in SemEval 2016 seldom adopt a deep learning method. This baseline is only for SemEval-2016 benchmark. * **ATAE-LSTM**[22]: This is an attention-based LSTM for ABSA, where the aspect vector for a target aspect is concatenated to all word embeddings in a text. Then, the word embeddings are used as an input of an LSTM and an attention layer is located on the top of the LSTM. Because the performance of ATAE-LSTM is not reported on SemEval 2016 Task 5, we implement it ourselves for comparison with the proposed model. * **GCAE**[20]: This is a CNN-based model for ABSA which includes the gated Tanh-ReLU units for capturing aspect-specific sentiment expressions from convolutional layers. The authors open their own code ([[https://github.com/wxue004](https://github.com/wxue004) cs/GCAE]([https://github.com/wxue004](https://github.com/wxue004) cs/GCAE)), and thus all experimental results on GCAE below are obtained using the code. * **AMSAN w/o aspect map**: Because the aspect vector of a target aspect is fed to the fully connected layer of AMSAN, AMSAN can operate without an aspect map from AMEN. Thus, the impact of the aspect map can be judged by comparing _AMSAN without aspect map_ with the proposed model. Table 2 reports the performances of all five models on the restaurant dataset. As shown in this table, the proposed model (AMEN-AMSAN) outperforms all baselines in both cases of with and without the sentiment polarity _conflict_. When _conflict_ is considered, the proposed model achieves 3% higher accuracy than UWB. Note that UWB is not an easy baseline in that it outperforms GCAE. ATAE-LSTM achieves the second-best performance with 82.699% mean accuracy. Even when the _conflict_ is ignored, the proposed model achieves the best performance with an accuracy of 86.578%, which is 1% higher than that of ATAE-LSTM and almost 3% higher than that of GCAE. AMSAN without the aspect map achieves relatively good performance with accuracies of 82.309% with _conflict_ and 85.394% without _conflict_. However, these accuracies are much lower than those of AMEN-AMSAN, which proves the impact of the aspect map in text-level ABSA. The proposed AMEN-AMSAN shows the best performance for the laptop dataset as well, as shown in Table 3. The proposed model improves UWB by 5.3% higher accuracy and outperforms both ATAE-LSTM and GCAE by 1.7% higher accuracies when _conflict_ is considered. Note that all deep learning models outperform UWB for this dataset. That is, both ATAE-LSTM and GCAE achieve nearly 3.6% higher accuracies than UWB. Without _conflict_, ATAE-LSTM and GCAE achieve the accuracies of 79.685% and 80.812%, respectively. However, they do not reach the performance of the proposed model which achieves an accuracy of 82.201%, and this is higher than that of ATAE-LSTM by 2.5% and that of GCAE by 1.4%. AMSAN without the aspect map also shows comparable results in both \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Acc. w/ Conflict** & **Acc. w/o Conflict** \\ \hline UWB & 81.931 (-) & - \\ ATAE-LSTM & 82.699 (0.996) & 85.496 (0.924) \\ GCAE & 81.640 (0.621) & 83.606 (0.756) \\ AMSAN w/o aspect map & 82.309 (0.546) & 85.394 (0.458) \\ AMEN-AMSAN & **83.936** (0.496) & **86.578** (0.407) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of the proposed model with its baselines on the restaurant dataset of SemEval-2016. cases of with and without _conflict_, which implies that AMSAN is a good model for text-level ABSA.