摘要
【目的】提出更适用公共政策评价的网民情感分类指标,引入深度学习技术研究网民立场的自动化识别和支持度研判问题。【方法】选取三个不同领域不同类型的重要公共政策作为研究对象,对微博数据进行采集、清洗和标注;运用立场分析方法研判三个政策的网民支持度;构建基于字符级卷积神经网络(CNN)技术的文本分类模型对实验数据集进行训练,并对实验结果进行对比检验解读。【结果】该模型在三组数据测试集的综合评价指标上均取得优秀表现,当模型稳定后有两组数据集F1值在0.8以上,一组数据集F1值在0.6以上;且耗时较循环神经网络(RNN)模型更短,训练时间差距达数十倍。【局限】数据样本量和政策覆盖类型有限,网民支持度计算方法有待进一步深化。【结论】立场分类方法和字符级CNN技术在公共政策评价的效度和效率上有较好表现,尤其在应急突发性政策评价方面能够发挥明显作用。
[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.
作者
邱尔丽
何鸿魏
易成岐
李慧颖
Qiu Erli;He Hongwei;Yi Chengqi;Li Huiying(Big Data Development Department,State Information Center,Beijing 100045,China;Department of Information Management,Peking University,Beijing 100871,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2020年第7期28-37,共10页
Data Analysis and Knowledge Discovery
基金
国家社会科学基金青年项目“使用大数据方法开展社会政策评估的探索性研究”(项目编号:18CSH018)的研究成果之一。
关键词
公共政策
立场分析
卷积神经网络
微博
大数据
Public Policy
Stance Detection
Convolutional Neural Network
Weibo
Big Data
