Evolution and Regional Differences of E-commerce Policies for Rural Poverty Reduction Based on Topic over Time Model
Yu Chuanming1(), Guo Yajing1, Gong Yutian1, Huang Manyu2, Peng Hufeng1
1School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China 2School of Business Administration, Zhongnan University of Economics and Law, Wuhan 430073, China
[Objective] This paper reveals the evolution and regional differences of E-commerce policies for rural poverty reduction from 2008 to 2017. [Methods] First, we used the ToT (Topic over Time) model to investigate the probability distributions of time-topics and topics-words related to E-commerce policies for rural poverty reduction. Then, we analyzed the evolution of the policy contents by calculating the average intensity of topics in each year and extracted the top n topic words with the highest probabilities. Third, we divided the data from each province into the eastern, central and western regions, and then analyzed the regional differences of policies according to the probability distribution of topics and words. [Results] E-commerce policies for rural poverty reduction had the starting, exploring and developing stages. The eastern, central and western regions have different focuses on logistics, platforms and personnel training. [Limitations] The regional differences of E-commerce policies need more fine-grained analysis. [Conclusions] Compared with the traditional word frequency counting method, the ToT model effectively reveals the policy evolution and their regional differences.
余传明, 郭亚静, 龚雨田, 黄漫宇, 彭虎锋. 基于主题时间模型的农村电商扶贫政策演化及地区差异分析*[J]. 数据分析与知识发现, 2018, 2(7): 34-45.
Yu Chuanming,Guo Yajing,Gong Yutian,Huang Manyu,Peng Hufeng. Evolution and Regional Differences of E-commerce Policies for Rural Poverty Reduction Based on Topic over Time Model. Data Analysis and Knowledge Discovery, 2018, 2(7): 34-45.
(Yang Yongchao.Innovative Development of Rural E-commerce in China under the Background of Supply-side Reform[J]. Review of Economic Research, 2017(18): 35-36. )
(Lv Yanwei, Liu Yang.Research on the Path of Promoting the Convergence and Development of Rural Primary, Secondary and Tertiary Industry[J]. Contemporary Economic Management, 2017, 39(10): 38-43.)
(Lu Zhaoyang.The Welfare Effects from the Development of the New Agricultural Management Entities[J]. The Journal of Quantitative & Technical Economics, 2016, 33(6): 41-58.)
(Liu Yajun.The Spontaneous Inclusive Growth under the Circumstance of Internet: A Longitudinal Case Study of “Taobao Village”[J]. Journal of Social Sciences, 2017(10): 46-60.)
(Cheng Chen, Ding Dong.“Internet+Agricultural E-commerce”: Development Path of Modern Agricultural Informatization[J]. Information Science, 2016, 34(11): 49-52, 59.)
(Wang Huimin, Wang Xin, Li Min.Internet + Agricultural, Exploring a New Mode of Rural E-commerce —— A Case Study of Sangua Commune[J]. Rural Economy and Science-Technology, 2017, 28(6): 75-76, 105.)
[8]
Feldman R.Text Mining[M]. Oxford University Press, 2002: 749-757.
[9]
Gabrilovich E, Markovitch S.Wikipedia-based Semantic Interpretation for Natural Language Processing[J]. Journal of Artificial Intelligence Research, 2009, 34(4): 443-498.
doi: 10.1613/jair.2669
[10]
Booth D E.Data Mining Methods and Models[J]. Technometrics, 2007, 49(4): 500.
doi: 10.1198/tech.2007.s697
(Wu Heng, Chen Yanling.Study on Information of Tourists’ Destination Selections Based on UGC and Text Mining——Taking Honeymoon Travel Notes from Ctrip as an Example[J]. Information Science, 2017, 35(1): 101-105.)
(Xu Yingmei, Gao Yiming.Construction of the Public Opinion Index of CPI Based on the Internet Big Data[J]. The Journal of Quantitative & Technical Economics, 2017(1): 94-112.)
(Meng Xuejing, Meng Xianglan, Hu Yangyang.Research on Investor Sentiment Index Based on Text Mining and Baidu Index[J]. Macroeconomics, 2016(1): 144-153.)
(Wu Lianren, Li Jinjie, Qi Jiayin.Research on Enterprise Public Opinions Based on Large-scale Text Data Sentiment Mining[J]. Knowledge Management Forum, 2016(6): 457-463.)
[16]
Setiawan J.Using Text Mining to Analyze Mobile Phone Provider Service Quality (Case Study: Social Media Twitter)[J]. International Journal of Machine Learning & Computing, 2014, 4(1): 106-109.
[17]
Blei D M, Lafferty J D.Dynamic Topic Models[C]// Proceedings of the 23rd International Conference on Machine Learning. 2006: 113-120.
[18]
Wang X, McCallum A. Topics over Time: A Non-Markov Continuous-Time Model of Topical Trends[C]// Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2006: 424-433.
(Shi Qingwei, Li Yanni, Guo Pengliang.Dynamic Finding of Authors’ Research Interests in Scientific Literature[J]. Journal of Computer Applications, 2013, 33(11): 3080-3083.)
doi: 10.11772/j.issn.1001-9081.2013.11.3080
(Fan Fengchun.A Retrospection and Reflection on the Equalization of Basic Public Services Since the Founding of PRC: A Discourse Analysis Approach[J]. The Journal of Shanghai Administration Institute, 2016, 17(1): 46-57.)
(Zhang Guoxing, Gao Xiulin, Wang Yingluo, et al.Measurement, Coordination and Evolution of Energy Conservation and Emission Reduction Policies in China: Based on the Research of the Policy Data from 1978 to 2013[J]. China Polulation, Resources and Environment, 2014, 24(12): 62-73.)
(Zhang Yongning, Li Hui, Cong Nan, et al.The Evolution and Reflection of China’s Reduction Policies under the Framework of Context-Expression-Result[J]. Science & Technology Progress and Policy, 2016, 33(20): 109-114.)
doi: 10.6049/kjjbydc.2016040445
(Zhang Yong’an, Yan Jin.Research on the Internal Structural Relation and Macro Layout of Scientific and Technological Achievements Transformation Policies Based on Text Mining[J]. Journal of Intelligence. 2016, 35(2): 44-49.)
doi: 10.3969/j.issn.1002-1965.2016.02.009
(Wang Yinhong, Li Mengzhu.Study on Local Government Attention of Ecological Environment Governance: Based on theText Analysis of Government Work Report in 30 Provinces and Cities (2006—2015)[J]. China Polulation, Resources and Environment, 2017, 27(2): 28-35.)
doi: 10.3969/j.issn.1002-2104.2017.02.006
(Li Huajiao, Zhang Hanjiang, Liu Nairong, et al.Temporal-Spatial Distribution Research Hot Topics of China’s Resources and Environment Carrying Capacity Based on On-Line News Reports[J]. Resources & Industries, 2016, 18(6): 27-32.)
doi: 10.13776/j.cnki.resourcesindustries.20161223.008
(Zhang Yong’an, Ma Yu.Quantitative Analysis of Regional Technological Innovation Policies with R Language[J]. Journal of Intelligence, 2017, 36(3): 113-118.)
doi: 10.3969/j.issn.1002-1965.2017.03.020
[28]
Python: An Interpreted High-level Programming Language for General-purpose Programming[EB/OL]. [2017-10-11].https://www.python.org/doc/.
(Shan Bin, Li Fang.A Survey of Topic Evolution Based on LDA[J]. Journal of Chinese Information Processing, 2010, 24(6): 43-49.)
doi: 10.3969/j.issn.1003-0077.2010.06.007
[30]
Blei D M.Latent Dirichlet Allocation[J]. Journal of Machine Learning Research, 2003, 3: 993-1022.