# Targeted Sentiment Classification Based on Attentional Encoding and Graph Convolutional Networks
## 1 Introduction  
Natural language processing is an important part of the new generation of artificial intelligence, particularly in human-machine interaction [1]. All the major computing companies have integrated or are integrating natural language processing capacity in their systems. Targeted sentiment classification [2, 3] is a basic task of natural language processing that has attracted a great deal of attention in recent years. It is a fine-grained task in sentiment analysis, and it aims to predict the emotional polarity of each target within a sentence. For example, in the sentence \"the price is reasonable while the service is poor\", the emotional polarities of two targets \"price\" and \"service\" are positive and negative, respectively. A specific target is usually an entity or an aspect term. Usually, researchers use machine learning algorithms to classify the sentiment of the given targets in a sentence. Some early work used handcraft features, such as sentiment lexicon and language bag-of-words features to train classifiers for a specific target sentiment classification [4, 5]. However,these methods are highly dependent on the quality of the selected features and require a large amount of manual feature engineering. In later studies, various neural-network-based methods became popular [6; 7], which did not need manual feature engineering. Most of them are based on long short-term memory (LSTM) neural networks [8], and some of them are convolutional neural networks (CNNs) [9]. Many of these neural-network-based methods embed specific target information into the sentence representation via an attention mechanism [7]. Some studies have applied attention mechanisms to generate target specific sentence representations [10; 11], or to transform sentence representations according to the target words [12]. However, these studies rely on complex recurrent neural networks (RNNs) as sequence encoders to infer the hidden semantics of the context. The first problem of the previous studies is that the semantic modeling only uses RNNs combined with the traditional attention mechanism. Each output state of RNNs depends on the previous state, while in semantic modeling, long-distance semantic information may be lost and the parallel computing of input data cannot be carried out [13]. In addition, the traditional attention mechanism is prone to introduce excessive noise because the distribution of weight values is too scattered, and thus it is difficult to accurately extract enough contextual sentiment information related to a specific target. Self-attention [14] is a novel attention mechanism. In sequence-to-sequence (Seq2 Seq) tasks, experimental results have demonstrated that this method performs more satisfactorily than traditional RNNs in capturing semantic information. Another problem in previous research is that these methods largely ignore the syntactic structure of the sentence, while in fact the syntactic structure helps to identify the emotional characteristics directly related to the specific target. When a specific target term is separated from its affective phrase, it is difficult to find related affective words in its surrounding words. The CNN-based models perceive multi-word features as continuous words by convolution of word sequences, whereas it is not sufficient to determine the sentiment expressed by multiple words that are not adjacent to each other [15]. Take the following sentence as an example: \"The hotpot, though served with poor service, is actually delicious\". As \"delicious\" and the target word \"hotpot\" in the word sequence are a little farther away from each other, the CNN models cannot capture the remote word dependency. On the other hand, in the syntactic dependency tree, the word \"delicious\" is closer to the target \"hotpot\" (see Figure 1). In addition, the use of syntactic dependency trees also helps to solve the potential ambiguity in word sequences [16]. In the simple sentence \"nice beef terrible juice\", nice and terrible can be used interchangeably.