Abstract
Sentiment Analysis is an essential research topic in the field of natural language processing (NLP) and has attracted the attention of many researchers in the last few years. Recently, deep neural network (DNN) models have been used for sentiment analysis tasks, achieving promising results. Although these models can analyze sequences of arbitrary length, utilizing them in the feature extraction layer of a DNN increases the dimensionality of the feature space. More recently, graph neural networks (GNNs) have achieved a promising performance in different NLP tasks. However, previous models cannot be transferred to a large corpus and neglect the heterogeneity of textual graphs. To overcome these difficulties, we propose a new Transformer-based graph convolutional network for heterogeneous graphs called Sentiment Transformer Graph Convolutional Network (ST-GCN). To the best of our knowledge, this is the first study to model the sentiment corpus as a heterogeneous graph and learn document and word embeddings using the proposed sentiment graph transformer neural network. In addition, our model offers an easy mechanism to fuse node positional information for graph datasets using Laplacian eigenvectors. Extensive experiments on four standard datasets show that our model outperforms the existing state-of-the-art models.
Highlights
With the rapid growth of textual content on the Internet such as social networks and e-commerce websites, the need for contextual processing and mining of the subjective information that text holds is increasing [1]
Deep learning models have attracted the attention of many researchers to address the problem of feature extraction. They propose various deep learning-based methods for sentiment analysis, which achieved promising results compared to machine learning methods in sentiment association and sentiment classification [3,9,10,11]
We propose a novel Sentiment Transformer Graph Convolutional Network (ST-GCN)
Summary
With the rapid growth of textual content on the Internet such as social networks and e-commerce websites, the need for contextual processing and mining of the subjective information that text holds is increasing [1]. Deep learning models have attracted the attention of many researchers to address the problem of feature extraction They propose various deep learning-based methods for sentiment analysis, which achieved promising results compared to machine learning methods in sentiment association and sentiment classification [3,9,10,11]. To the best of our knowledge, this is the first study to model the sentiment corpus as a heterogeneous graph and learn document and word embeddings using the proposed text graph transformer network; Inspired by the widespread use of positional encoding in NLP transformer models and current research on node positional features in GNNs, our model offers an easy mechanism to fuse node positional information for graph datasets using Laplacian eigenvectors; Results on several sentiment benchmark datasets demonstrate that our model outperforms the state-of-the-art sentiment classification methods
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