# Aspect-Based Sentiment Analysis Using Aspect Map
## 1 Introduction
The first CNN of the proposed model extracts an aspect map for a target aspect where the aspect map indicates significant words in a text with respect to the target aspect, and the other CNN determines the sentiment polarity of the target aspect by referring to the aspect map. Note that a corpus where the aspects are annotated at word level is needed to learn aspect map extraction. However, such a corpus is not available as stated above. Therefore, the aspect map should be extracted in a weakly supervised manner. Recently, the class activation map (CAM) has been proposed for weakly supervised object localization in the community of computer vision [24]. It localizes the objects of an image by linking the feature maps of CNN to relevant classes where the linking is implemented by global average pooling. Thus, the network supported by CAM can learn the localization of objects from the weakly labeled images, and is similar to the aspect map extraction from weakly labeled texts. The CNNs for text analysis usually have a single convolutional layer with various kernel sizes unlike those for image analysis [10]. Figure 1: An example of aspect-based sentiment analysis (ABSA). Sentiment expressions such as “nice” or “bad” appear near their related aspect expressions “view of river” and “sushi rolls,” respectively. Thus, they can capture \(n\)-gram word patterns for various \(n\)s. As a result, multiple CAMs are generated according to \(n\) and the number of different kernels. The aspect map for a target aspect in ABSA is a summation of all these CAMs. After the aspect map is extracted, the second CNN classifies the sentiment of a target aspect. The convolutional layer of this CNN produces feature maps that activate the sentiment expressions such as \"_nicc_\" and \"_bad_\" in Figure 1. Generally, in ABSA, only the sentiment expressions near the aspect expressions of a target aspect are under interest. Thus, the aspect map is applied to the feature maps of sentiment expressions to leave the target aspect-specific sentiment feature maps. Then, the second CNN determines the sentiment polarity of a text for the target aspect using the aspect-specific sentiment feature maps. In the recent work of [PERSON] and [PERSON] [20], a gated convolutional neural network is introduced for ABSA. Similar to our work, this model generates both sentiment and aspect features by using tanh gates and relu gates with aspect embeddings respectively. At every position, aspect features from relu gates are used to amplify sentiment features from tanh gates, thus the gated CNN tries to solve the problem of the context vector of attention mechanisms. Unlike this work which just utilizes aspect embeddings to generate aspect features and learns all features under the sentiment classification objective, the proposed model learns the relationship between words and aspects separately from the sentiment classification. Consequently, the proposed model can obtain a certain aspect map and can effectively exploit the aspect map for ABSA. The rest of this paper is organized as follows. In Section 2, we briefly introduce previous work on recent neural models for aspect-based sentiment analysis. In Section 3, we describe the overall process of the proposed model, and then, in Section 4, we explain the detailed architectures and mechanisms of two CNNs used in the proposed model. The experimental results are given in Section 5. Finally, we conclude the study in Section 6.