Automatic Transferring Government Website E-Mails Based on Text Classification
Wang Sidi1,2,Hu Guangwei1,2(),Yang Siyu1,2,Shi Yun1
1School of Information Management, Nanjing University, Nanjing 210023, China 2Government Data Resources Institution of Nanjing University, Nanjing 210023, China
[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 e-mails, which improves the efficiency of online government and reduces administrative costs.
王思迪,胡广伟,杨巳煜,施云. 基于文本分类的政府网站信箱自动转递方法研究*[J]. 数据分析与知识发现, 2020, 4(6): 51-59.
Wang Sidi,Hu Guangwei,Yang Siyu,Shi Yun. Automatic Transferring Government Website E-Mails Based on Text Classification. Data Analysis and Knowledge Discovery, 2020, 4(6): 51-59.
( Sun Zongfeng, Zhao Xinghua. A Study on the Interaction Between the Government and the People in the Internet-Based on the Big Data Analysis of the Mayor’s Mailbox of Qingdao[J]. E-Government, 2019(5):12-26.)
( Yu Junbo, Li Huilong, Yu Shuman. Responsiveness in “Governing Online”—An Exploratory Study on K City’s Leader Mailbox[J]. Changbai Journal, 2018(2):65-74.)
( Zheng Juntian, Gao Yuanying, Gu Qing. Practice and Perfection of Local Governmental Administrative Power List System Construction[J]. Chinese Public Administration, 2016(2):6-9.)
( Wang Jun. Research on Electronic Archive Automatic Classification System Based on Text Feature Recognition[J]. Modern Electronics Technique, 2019,42(18):45-49.)
( Li Xiangdong, Xu Peng, Huang Li, et al. Research of Journals Manuscript Categorization Based on KNN Algorithm[J]. Document, Information & Knowledge, 2010(4):71-76.)
[6]
李成铭. 基于文本特征提取技术的在线人职匹配研究及应用[D]. 成都:电子科技大学, 2017.
[6]
( Li Chengming. Research and Application of Talent Job Online Matching Based on Text Feature Extraction Technology[D]. Chengdu:University of Electronic Science and Technology of China, 2017.)
( Wang Ruojia, Zhang Lu, Wang Jimin. Automatic Triage of Online Doctor Services Based on Machine Learning[J]. Data Analysis and Knowledge Discovery, 2019,3(9):88-97.)
[8]
Kim K, Zzang S Y. Trigonometric Comparison Measure: A Feature Selection Method for Text Categorization[J]. Data & Knowledge Engineering, DOI: 10.1016/j.datak.2018.10.003.
doi: 10.1016/j.datak.2011.03.009
pmid: 21765568
[9]
Ghareb A S, Bakara A A Al-Radaideh Q A, et al. Enhanced Filter Feature Selection Methods for Arabic Text Categorization[J]. International Journal of Information Retrieval Research (IJIRR), 2018,8(2):1-24.
[10]
Hartmann J, Huppertz J, Schamp C, et al. Comparing Automated Text Classification Methods[J]. International Journal of Research in Marketing, 2019,36(1):20-38.
doi: 10.1016/j.ijresmar.2018.09.009
( Tian Huan, Li Honglian, Lv Xueqiang, et al. Text Categorization of Academic Activities Based on an Improved BP Neural Network[J]. Journal of Beijing Information Science & Technology University, 2018,33(5):38-44.)
( Liu Liu, Wang Dongbo. Identifying Interdisciplinary Social Science Research Based on Article Classification[J]. Data Analysis and Knowledge Discovery, 2018,2(3):30-38.)
[13]
Gauld R, Flett J, McComb S, et al. How Responsive are Government Agencies When Contacted by Email? Findings from a Longitudinal Study in Australia and New Zealand[J]. Government Information Quarterly, 2016,33(2):283-290.
doi: 10.1016/j.giq.2016.03.004
( Li Huilong, Yu Junbo. The Responsive Trap of Digital Government Governance-Based on the Investigation of “Message Board of Local Leaders” in Three Northeastern Provinces[J]. E-Government, 2019(3):72-87.)
[15]
Ong C S, Wang S W. Managing Citizen-Initiated Email Contacts[J]. Government Information Quarterly, 2009,26(3):498-504.
doi: 10.1016/j.giq.2008.07.005
( Hu Jiani, Xu Weiran, Guo Jun, et al. Study on Feature Selection Methods in Chinese Text Categorization[J]. Study on Optical Communications, 2005(3):44-46.)