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
[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 . [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.
范涛,王昊,吴鹏. 基于图卷积神经网络和依存句法分析的网民负面情感分析研究*[J]. 数据分析与知识发现, 2021, 5(9): 97-106.
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.
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