# Aspect-Based Sentiment Analysis Using Aspect Map
## 1 Introduction  
Personal opinions are prevalent these days as considerable reviews and comments are available on various websites such as IMDb.com, Amazon.com and Yelp.com. The opinions in these texts are used in analyzing the reputation of movies, improving products, and providing a recommendation on personal items such as books or restaurants. Sentiment analysis is a well-known opinion-mining task, which determines whether a text contains a positive opinion or a negative one. Since the texts created by users are often noisy as they include a number of grammatical errors and emoticons, it is necessary to apply some proper text preprocessing to the texts before analyzing their sentiments [1]. Various sentiment analysis approaches have been developed upon the preprocessed texts such as lexicon-based methods [2; 3; 4; 5], statistical machine learning methods [6; 7; 8; 9], and recent deep learning methods [10; 11; 12; 13; 14]. In general, a single text contains multiple aspects and the aspects are expressed independently. Thus, for in-depth sentiment analysis, the sentiments of a text should be examined at aspect level,not at text level. Several methods have been proposed for aspect-based sentiment analysis (ABSA) [15]. Because a target aspect is fixed in ABSA, it is important to properly exploit the information for the aspect within a text, as well as to understand the text. For understanding a text, deep learning has been proven to be a dominant method [10; 16; 17]. As a result, convolutional neural networks (CNNs) and bi-directional recurrent neural networks (bi-RNNs) are widely used as a text encoder in ABSA [18; 19; 20; 21]. For exploiting the information of a target aspect in ABSA, [PERSON] et al. employed the aspect vector of a target aspect [18], where the vector is generated by embedding aspect categories onto a continuous vector space. Then, they determined the aspect-level sentiment of a text with a concatenated vector of the aspect vector and a text vector. However, this simple application of the target aspect information has a weakness of dealing with all words in a text with the same importance. Many RNN-based ABSA models have solved this problem by adopting attention mechanisms [21; 22; 23]. Because attention mechanisms have an ability to focus on salient words for the task of interest, they have been used to generate a context vector that selectively embodies informative expressions for aspect-level sentiment classification. The main problem of the context vector is that it loses positional information of salient words, even if the information is important in ABSA to know where the aspect expressions of a target aspect appear. For instance, Figure 1 shows an example review on a restaurant with two different target aspects and their corresponding sentiment expressions. In this figure, one target aspect is _LOCATION_ represented with an aspect expression \"_view of river_\", and the other is _FOOD_ expressed as \"_sushi rolls_\". Note that the positive opinion about _LOCATION_ can be found by looking at the leftmost word \"_nice_\". Similarly, the negative word \"_bad_\" for the aspect _FOOD_ is found near the aspect expression \"_sushi rolls_\". That is, the sentiment expressions appear near their aspect expressions. If a corpus in which aspect expressions and their aspect labels are annotated at word level is available, the positional information of the aspect expressions can be easily incorporated into the existing ABSA methods. However, such a corpus is not available in general, but only the corpora in which the sentiments are annotated at text level are ample. In addition, it is extremely tedious and labor-intensive to construct a corpus with word-level annotations. This paper proposes a novel deep neural network that consists of two CNNs, to exploit the proximity property between an aspect expression and its sentiment mention.