Shenxing Xu
ABSTRACT
Aspect-level sentiment analysis is a sub-task of fine-grained sentiment analysis, which aims to predict the sentiment polarity of specific entities or aspects in the identified text to provide richer sentiment information. In recent years, notable advancements have been achieved in the realm of aspect-level text sentiment analysis research. However, the existing methods still have the problems that the traditional pre-trained model cannot solve the problem of multiple meanings of a word, and the existing attention mechanism model cannot recognize context words as sentiment aspect words accurately. Therefore, an aspect-level text sentiment analysis model based on XLNet-GCN is proposed in this paper. Firstly, XLNet is used to generate dynamic word vector representations of text sequences. Then, the text vectors are inputted into BiLSTM to extract text features, and atop the BiLSTM output, multi-layer graph convolution is executed to acquire aspect-specific features. Finally, the sentiment polarity is computed as the model output. Compared with the classical aspect-specific graph convolutional neural network model (ASGCN), our proposed model has improved the accuracy and F1 value by 0.08%∼1.55% and 0.84%∼2.01% in five benchmark datasets.
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Digital Library
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Index Terms
- Research on Aspect-based Sentiment Analysis Based on XLNet-GCN
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