基于文本分类的政府网站信箱自动转递方法研究

Automatic Transferring GovernmentWebsite E-Mails Based on Text Classification

【目的】为改善政府网站领导信箱传统人工转递方式存在的人力、时间成本较高以及工作人员负担较重等问题,研究网站来信的自动转递方法。【方法】选择较有代表性的分类算法,包括朴素贝叶斯、决策树、随机森林以及多层神经网络,对北京、合肥和深圳的市长信箱文本数据进行对比实验,进而设计一套基于文本分类的政府网站信箱自动转递方法,并给出相应的应用建议。【结果】神经网络算法在市长信箱文本的分类表现最优,宏平均精确度和召回率均达0.85以上,且所有微平均指标均达0.93以上;朴素贝叶斯算法次之;随机森林算法的宏平均精确度很高,但召回率较差;决策树算法的精确度和召回率都较一般。【局限】未能兼顾来信数量不均衡对结果的影响,且实验时剔除了数据量过小的部门的来信数据,这在实际应用中可能会存在一定偏差。【结论】本文设计的政府网站信箱自动转递方法能够优化领导信箱运作机制,对提升线上政民互动效率,降低人力及行政成本具有积极意义。

[Objective]This research proposes a method to automatically transferring e-mails received by government websites,aiming to reduce labor costs of managing public email boxes.[Methods]First,we chose four representative classification algorithms,including Naïve Bayes,Decision Tree,Random Forest and Multi-Layer Perception,and compared their classification resutls of e-mails received by the websites of Mayor’s Offices in Beijing,Hefei and Shenzhen.Then,we designed a method of automatically transferring these emails.Finally,we gave suggestions on the application of our method in the real world settings.[Results]Multi-Layer Perception yielded the best performance in our study,with the macro average precision and recall reaching more than 0.85,and all micro average indicators reaching more than 0.93.Naïve Bayes took the second place.Random Forest had a high macro average precision,but poor recall score.Decision Tree had an average precision and recall results.[Limitations]We did not examine the impacts of skewed distribution of received emails and eliminated the departments receiving few emails.[Conclusions]The proposed method optimizes the operation of public emails,which improves the efficiency of online government and reduces administrative costs.