# Aspect-Based Sentiment Analysis Using Aspect Map
## 2 Related Work  
Aspect-based sentiment analysis has been regarded as an important task in opinion mining [15]. Because CNNs and RNNs have proven their superiority for text understanding without labor-intensive feature engineering, most recent studies lean toward deep learning. Early deep learning methods generally used a target aspect as a kind of context information for aspect-aware sentiment classification [18; 25]. For the classification, the previous studies embedded a target aspect onto a context vector, and fed the vector to the classification layer together with a text vector encoded by a CNN or an RNN. However, because a text is encoded independently from the target aspect, these methods fail in detecting specific expressions for the target aspect.  
Recently, attention mechanisms have been employed to solve this problem. In ATAE-LSTM [22], the aspect vector is concatenated to every word representation, and then an attention mechanism finds proper sentiment expressions of the aspect vector. The attention mechanism in ATAE-LSTM can capture the relation between aspects and aspect-specific sentiment words like _price/cheap_, but fails in classifying the text in Figure 1 due to general sentiment words such as \"_nicc_\" and \"_bad_\". Because general sentiment words are used with too many aspects, the attention mechanism cannot detect their correct aspect.  
Gated convolutional network with aspect embedding (GCAE) [20] is a CNN-based model that includes two different gate units on top of two convolutional layers. It first generates two different feature maps from two convolutional layers, and then each feature map is delivered to the tanh gate and the ReLU gate, respectively. The ReLU gate receives additional information to control the propagation of sentiment features. After the outputs of the two gates are combined by element-wise multiplication, the combined features are delivered to the classification layer. The gating mechanism of the ReLU gate is applied only to \(n\)-gram local features. Thus, it is possible to detect aspect-specific sentiment features within the distance of kernel size from aspect expressions. However, the convolutional layer with the ReLU gate in GCAE must match both aspect and sentiment expressions, which leads to performance deterioration.  
[PERSON] et al. demonstrated the usefulness of the positional information of aspect expressions in ABSA [19]. They introduced the location attention for ABSA in their study. To utilize the proximity between a sentiment expression and an aspect expression, the location attention is calculated by measuring the absolute distance of each word to the aspect expression. With this location attention, the words that appear near the aspect expression are more focused for sentiment classification. They were successful in showing the effectiveness of the positional information of aspect expressions. However, because their method requires aspect expressions for the target aspect as its input, it does not work when only text-level annotations are available.  
[PERSON] et al. applied class activation mapping to the task of sentiment classification [26]. They trained multi-channel CNNs on movie reviews, and then demonstrated sentiment-specific expressions mined by CAMs. Their method for obtaining CAMs is partially similar to the proposed model, but different in that the original CAM [24] was used as it is in their work. Thus, what they have shown is just the word localization ability of CAM. On the other hand, the proposed model adopts CAM to solve ABSA.