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Research on Aspect-based Sentiment Analysis Based on XLNet-GCN

Published: 28 February 2024 Publication History

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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  1. Research on Aspect-based Sentiment Analysis Based on XLNet-GCN

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    ICCPR '23: Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition
    October 2023
    589 pages
    ISBN:9798400707988
    DOI:10.1145/3633637
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 28 February 2024

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    Author Tags

    1. Aspect-based sentiment analysis,XLNet
    2. Graph Convolutional Networks

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    • Research-article
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    • Refereed limited

    Funding Sources

    • Discipline with Strong Characteristics of Liaocheng University -- Intelligent Science and Technology
    • National Natural Science Foundation of China

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    ICCPR 2023

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