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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (9): 97-106    DOI: 10.11925/infotech.2096-3467.2021.0146
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Sentiment Analysis of Online Users' Negative Emotions Based on Graph Convolutional Network and Dependency Parsing
Fan Tao1,Wang Hao1(),Wu Peng2
1School of Information Management, Nanjing University, Nanjing 210023, China
2School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China
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Abstract  

[Objective] This paper develop news method to improve the negative sentiment analysis of online users. [Objective] 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.

Key wordsOnline Public Opinion      Negative Emotions      Graph Convolutional Network      Self-Attention      Dependency Parsing     
Received: 11 February 2021      Published: 15 October 2021
ZTFLH:  分类号: G202  
Fund:*National Natural Science Foundation of China(71774084);Interdisciplinary Project of Nanjing University(010814370113)
Corresponding Authors: Wang Hao     E-mail: ywhaowang@nju.edu.cn

Cite this article:

Fan Tao,Wang Hao,Wu Peng. Sentiment Analysis of Online Users' Negative Emotions Based on Graph Convolutional Network and Dependency Parsing. Data Analysis and Knowledge Discovery, 2021, 5(9): 97-106.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.0146     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I9/97

The Image of Denpendency Parsing
The Negative Sentiment Analysis Model of Online Users Based on GCN and Dependency Parsing
An Example of the Dependency Parsing Tree and Its Corresponding Adjacent Matrix
情感 数量
非负面 22 536
负面 愤怒 6 839
厌恶 6 645
恐惧 5 008
悲伤 5 584
Results of the Sentiment Annotation
模型 Acc / % Macro _ P / % Macro _ R / % Macro _ F 1 / %
BiLSTM-Attention-GCN 84 . 842 84 . 941 85 . 143 84 . 937
BiLSTM-GCN 82.766 82.879 82.851 82.592
BiLSTM-attention 81.042 80.946 80.951 80.923
BiLSTM 79.610 79.749 79.085 79.219
CNN 77.990 78.416 77.042 77.347
Logistic 73.173 73.041 73.868 72.981
Results of Online Users' Negative Sentiments
Confusion Matices of Different Sentiment Classification Models
The Impact of Hidden Units on the Performance of the Model
The Impact of the Number of GCN Layers on the Performance of the Model
模型 Acc / % Marco _ P / % Marco _ R / % Marco _ F 1 / %
BiLSTM-Attention-GCN 82 . 667 82 . 657 82 . 68 82 . 665
BiLSTM-GCN 82.098 82.089 82.058 82.070
BiLSTM-Attention 81.712 81.723 81.650 81.673
BiLSTM 81.401 81.409 81.340 81.362
CNN 79.835 79.814 79.807 79.810
Logistic 74.225 74.197 74.187 74.192
Results of Two Categories Classification of Online Users' Sentiments
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