[Objective] This paper proposed an index of Internet users’ sentiment classification which is more suitable for public policy evaluation, and explored the automatic method for Internet users’ stance detection based on the deep learning technology.[Methods] Three important public policies of different types and in different fields were selected as research objects. After collecting, cleaning and labeling the related data of Sina Weibo, this paper analyzed the three policies’ support on Internet, and constructed a text classification model based on the character-level convolutional neural network (CNN) technology. Meanwhile this paper compared and interpretd the effectiveness and efficiency of the experimental results.[Results] The results showed that our model can achieve good performance on the indicators of the accuracy and recall rate of the three datasets.There were two datasets with F1 value above 0.8 and one dataset with F1 value above 0.6. Meanwhile the model took less time than the recurrent neural network (RNN) model, and the training time gap is dozens of times.[Limitations] The data sample size and policy coverage are limited, and the calculation method for Internet users’ support needs to be further studied.[Conclusions] The stance classification method and the character-level CNN technology perform well in the effectiveness and efficiency of public policy evaluation, and may play a significant role especially in the evaluation of emergency policies.
邱尔丽,何鸿魏,易成岐,李慧颖. 基于字符级CNN技术的公共政策网民支持度研究 *[J]. 数据分析与知识发现, 2020, 4(7): 28-37.
Qiu Erli,He Hongwei,Yi Chengqi,Li Huiying. Research on Public Policy Support Based on Character-level CNN Technology. Data Analysis and Knowledge Discovery, 2020, 4(7): 28-37.
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