【目的】提出更適用公共政策評價的網民情感分類指標,引入深度學習技術研究網民立場的自動化識別和支持度研判問題。【方法】選取三個不同領域不同類型的重要公共政策作為研究對象,對微博數據進行采集、清洗和標注;運用立場分析方法研判三個政策的網民支持度;構建基于字符級卷積神經網絡(CNN)技術的文本分類模型對實驗數據集進行訓練,并對實驗結果進行對比檢驗解讀。【結果】該模型在三組數據測試集的綜合評價指標上均取得優秀表現,當模型穩定后有兩組數據集F1值在0.8以上,一組數據集F1值在0.6以上;且耗時較循環神經網絡(RNN)模型更短,訓練時間差距達數十倍。【局限】數據樣本量和政策覆蓋類型有限,網民支持度計算方法有待進一步深化。【結論】立場分類方法和字符級CNN技術在公共政策評價的效度和效率上有較好表現,尤其在應急突發性政策評價方面能夠發揮明顯作用。

[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.