# Graph Convolutional Networks with POS Gate for Aspect-Based Sentiment Analysis
###### Abstract  
We make daily comments on online platforms (e.g., social networks), and such natural language texts often contain sentiment (e.g., positive and negative) for certain aspects (e.g., food and service). If we can automatically extract the aspect-based sentiment from the texts, then it will help many services or products to overcome their limitations of particular aspects. There have been studies of aspect sentiment classification (ASC) that finds sentiment towards particular aspects. Recent studies mostly adopt deep-learning models or graph neural networks as these techniques are capable of capturing linguistic patterns that contributed to performance improvement in various natural language processing tasks. In this paper, for the ASC task, we propose a new hybrid architecture of graph convolutional network (GCN) and recurrent neural network. We design a gate mechanism that jointly models word embeddings and syntactic representation of sentences. By experimental results on five datasets, we show that the proposed model outperforms other recent models and also verify that the gate mechanism contributes to the performance improvement. The overall F1 scores that we achieved is 66.64\(\sim\)76.80%. graph convolutional network; aspect-based sentiment analysis; POS gate; recurrent neural; Word LSTM +
Footnote †: journal: Applied  
2014 Task 4 subtask 2 restaurant domain (REST14). [PERSON] et al. [5] used naive Bayes classifier with a feature selection algorithm, and they achieved a 78% of F1 score for the REST14 dataset. Although these studies have shown successful performance, they are limited because they require significant effort in feature engineering. Deep learning (DL) models are favorable as they find arbitrary features automatically from data [6]. There are a few well-known types of DL models: multi-layered perceptron (MLP) [7], recurrent neural networks (RNN) [8], convolutional neural networks (CNN) [9], and attention mechanism [10]. These different DL models have their own merits, as discussed in [11]; for example, some studies employed CNN to tackle the task of sentiment classification as the CNN is known to be efficient and effective in analyzing local patterns [12; 13]. In particular, attention-based RNN architecture was often adopted in many studies of the ASC task as the attention-based architecture has shown its effectiveness in analyzing sequential patterns beneath the word sequences of natural language texts [10; 14]. [PERSON] et al. [15] proposed an attention-based RNN model using long short-term memory (LSTM) [16], and achieved 78% accuracy for REST14. [PERSON] et al. [17] proposed bi-directional LSTM (Bi-LSTM) with a fine-grained attention mechanism, that captures the word-level interaction between aspect and context. They achieved 81% accuracy for REST14. Recently, a graph neural network is widely used to incorporate graph-like patterns such as dependency between words for the ASC task. [PERSON] and [PERSON] [18] proposed a hybrid architecture of LSTM and a graph neural network, and adopted other resources (e.g., DBpedia and Wikipedia) to train their model. They achieved 87% accuracy for REST14. [PERSON] et al. [19] utilized pre-trained Bidirectional Encoder Representations from Transformers (BERT) [20] and graph attention network (GAT) on the dependency tree, and achieved 85% accuracy for REST14. Using context-aware pre-trained language models (e.g., BERT) has drawn much attention as it allows us to achieve better accuracy; however, it requires relatively large computational cost compared to context-free language models (e.g., Word2 Vec [21] and Glove [22]), as reported in [23]. There are studies that adopt neither context-aware pre-trained models nor other resources. [PERSON] et al. [24] proposed building a graph convolutional network (GCN) over the dependency tree of a sentence to exploit syntactical information and word dependencies, and achieved 80% accuracy for the REST14 dataset. [PERSON] et al.