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
收藏
页码：97 / 106
页数：9
相关论文
共 32 条
[21]  
Bastings J, Titov I, Aziz W, Et al., Graph Convolutional Encoders for Syntax-aware Neural Machine Translation, Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 1957-1967, (2017)
[22]  
Nguyen T H, Grishman R., Graph Convolutional Networks with Argument-Aware Pooling for Event Detection[C], Proceedings of the AAAI Conference on Artificial Intelligence, (2018)
[23]  
Mikolov T, Chen K, Corrado G, Et al., Efficient Estimation of Word Representations in Vector Space[OL]
[24]  
Gers F A, Schmidhuber J, Cummins F., Learning to Forget: Continual Prediction with LSTM, Neural Computation, 12, 10, pp. 2451-2471, (2000)
[25]  
Wu Peng, Liu Hengwang, Shen Si, Sentiment Analysis of Network Public Opinion Based on Deep Learning and OCC, Journal of the China Society for Scientific and Technical Information, 36, 9, pp. 972-980, (2017)
[26]  
Ekman P., An Argument for Basic Emotions, Cognition and Emotion, 6, 3-4, pp. 169-200, (1992)
[27]  
Tromp E, Pechenizkiy M., Rule-based Emotion Detection on Social Media: Putting Tweets on Plutchik's Wheel[OL]
[28]  
Che W X, Li Z H, Liu T., LTP: A Chinese Language Technology Platform, Proceedings of the 23rd International Conference on Computational Linguistics: Demonstrations, pp. 13-16, (2010)
[29]  
Xie J, Chen B, Gu X L, Et al., Self-Attention-Based BiLSTM Model for Short Text Fine-Grained Sentiment Classification, IEEE Access, 7, pp. 180558-180570, (2019)
[30]  
Xu G X, Meng Y T, Qiu X Y, Et al., Sentiment Analysis of Comment Texts Based on BiLSTM, IEEE Access, 7, pp. 51522-51532, (2019)
1234