# Aspect-Based Sentiment Analysis Using Aspect Map
## 5 Experiments
### Quality of Aspect Map  
To see how the aspect map is improved by the \(L_{1}\) regularizer \(\|\mathcal{A}^{t}\|_{1}\) in Equation (7), two aspect maps for three aspects are shown in Figure 8 when a restaurant review is given. The three aspects are _FOOD#QUALITY_, _AMBIENCE#GENERAL_ and _FOOD#PRICES_. First, let us see the aspect maps in Figure 8a which are not regularized yet by the proposed \(L_{1}\) regularizer. In the aspect map for _FOOD#QUALITY_, the phrases _\"restaurant,\" _\"The pizza is,\"_ and _\"the atmosphere. But the pizza is\"_ are strongly highlighted. Among them, the expression of _\"the pizza is\"_ is relevant to the target aspect _FOOD#QUALITY_, but the other expressions such that _\"restaurant\"_ and _\"the atmosphere.\"_ are not much related to the target aspect. In the aspect map for _AMBIENCE#GENERAL_, the phrases of _\"a fun restaurant\"_ and _\"the atmosphere. But\"_ are correctly specified as the aspect expressions of _AMBIENCE#GENERAL_, while _\"yummy\"_ is also activated though it is not related to _AMBIENCE#GENERAL_. Similarly, the aspect map for _FOOD#PRICES_ focuses well on the expression of _\"way too expensive,\"_ but it also focuses badly on the expression of _\"is yummy.\"_ As seen in these examples, the aspect maps basically find true aspect expressions for their target aspects, but false aspect expressions are also often highlighted by the aspect maps. It is obvious that such noises prevent AMSAN from classifying sentiment of a target aspect correctly. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Acc. on Whole Test** & **Acc. on Hard Test** \\ \hline ATAE-LSTM & 74.87 (1.46) & 49.89 (1.88) \\ GCAE & 74.13 (0.72) & 46.29 (1.93) \\ AMSAN w/o aspect map & 74.59 (0.78) & 46.52 (1.28) \\ AMEN-AMSAN & **75.59** (0.51) & **53.26** (0.41) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of the proposed model with its baselines on the restaurant dataset of SemEval-2014. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Acc. w/ Conflict** & **Acc. w/o Conflict** \\ \hline UWB & 75.046 (-) & - \\ ATAE-LSTM & 78.692 (0.747) & 79.685 (1.197) \\ GCAE & 78.659 (0.812) & 80.812 (0.609) \\ AMSAN w/o aspect map & 79.719 (0.920) & 81.797 (1.100) \\ AMEN-AMSAN & **80.399** (0.555) & **82.201** (0.488) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of the proposed model with its baselines on the laptop dataset of SemEval-2016. On the other hand, Figure 8b shows the aspect expressions of the aspect maps penalized by the proposed regularizer. The expressions in this figure are activated more sparsely but more precisely than those in Figure 8a. The aspect map for _FOOD#QUALITY_ no longer highlights the expressions \"_restaurant_\" and \"_the atmosphere_\", but newly highlights the expression \"_yummy._\" In the aspect map for _AMBIENCE#GENERAL_, it can be observed that the activation of the expression \"_yummy_\" is weakened compared to the original aspect map in Figure 8a. Finally, \"_is yummy_,\" the strongly specified expression in the original aspect map for _FOOD#PRICES_, is also deactivated in the regularized aspect map. Due to the refined aspect maps, AMSAN is able to classify the sentiment polarity of a text correctly for a given target aspect. For instance, the expression \"_the atmosphere. But_\" helps AMSAN classify the review in Figure 8 as _positive_ for the target aspect _AMBIENCE#GENERAL_, because it appears near the word \"_like_\" which AMSAN concentrates on. Another conspicuous characteristic of the aspect map is that it tends to highlight the sentiment expressions as well as aspect expressions. For instance, \"_the atmosphere_\" and \"_fun_\" are highlighted by the aspect map for _AMBIENCE#GENERAL_, where \"_fun_\" is a sentiment expression and \"_the atmosphere_\" is an aspect expression.