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

doi:10.11925/infotech.2096-3467.2021.0146

Data Analysis and Knowledge Discovery

2021, Vol. 5

Issue (9): 97-106 DOI: 10.11925/infotech.2096-3467.2021.0146

Current Issue | Archive | Adv Search

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 words： Online 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

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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

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	$82.098$	$82.089$	$82.058$	$82.07$ 0
BiLSTM-Attention	$81.712$	$81.723$	$81.650$	$81.673$
BiLSTM	$81.401$	$81.409$	$81.340$	$81.362$
CNN	$79.835$	$79.814$	$79.807$	$79.81$ 0
Logistic	$74.225$	$74.197$	$74.187$	$74.192$

Results of Two Categories Classification of Online Users' Sentiments

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