Sentiment Analysis of Online Users'Negative Emotions Based on Graph Convolutional Network and Dependency Parsing

被引：0

作者：

Fan T. ^{[1
]}

Wang H. ^{[1
]}

Wu P. ^{[2
]}

机构：

[1] School of Information Management, Nanjing University, Nanjing

[2] School of Economics and Management, Nanjing University of Science and Technology, Nanjing

来源：

Data Analysis and Knowledge Discovery | 2021年 / 5卷 / 09期

基金：

中国国家自然科学基金;

关键词：

Dependency Parsing; Graph Convolutional Network; Negative Emotions; Online Public Opinion; Self-Attention;

D O I：

10.11925/infotech.2096-3467.2021.0146

中图分类号：

学科分类号：

摘要：

[Objective] This paper develop news method to improve the negative sentiment analysis of online users. [Methods] We proposed a model based on Graph Convolutional Networks (GCN) and dependency parsing. This model combined the BiLSTM and attention mechanism to extract textual features, which were then used as the vertex features. Third, we utilized the GCN to train the vertex features and the corresponding adjacency matrices. Finally, the model generated four types of emotions (anger, disgust, fear and sadness). [Results] We conducted an empirical study with online public opinion datasets (i. e.,“COVID-19”) and compared the performance of our model with the baseline models. We found that the proposed model has certain advantages. For the emotion of“fear”, the recognition accuracy reached 93.535%. [Limitations] We only examined the proposed model with online public opinion datasets. More research is needed to evaluate its performance with other public datasets. [Conclusions] Combining the dependency parsing information, the GCN, and the attention mechanism could increase the performance of negative sentiment analysis. © 2023 Chin J Gen Pract. All rights reserved.

引用

页码：97 / 106

页数：9

共 32 条

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