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
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摘要
【目的】揭示农村电商扶贫政策从2008年到2017年的演化规律以及区域差异。【方法】运用主题时间模型,提取10年间农村电商扶贫政策的时间–主题概率分布以及主题–词汇概率分布,通过计算不同年份下主题的平均强度并提取每个主题下概率高的前n个词汇,分析政策内容的演化情况;将各省数据按照东、中、西部进行划分,得到各个地域的主题–词汇概率分布,分析政策的区域差异性。【结果】农村电商扶贫政策经历了起步、探索、发展三个阶段,东、中、西部地区在物流、平台、人才培养等方面具有不同侧重点。【局限】农村电商扶贫政策的区域差异分析有待细化。【结论】相比传统的词频统计方法,主题时间模型更为清晰、准确地揭示了政策演化规律与区域差异。
[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 To T(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 To T model effectively reveals the policy evolution and their regional differences.
[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 To T(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 To T model effectively reveals the policy evolution and their regional differences.
引文
[1]杨永超.供给侧改革背景下我国农村电商创新发展路径[J].经济研究参考,2017(18):35-36.(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.)
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[3]鲁钊阳.新型农业经营主体发展的福利效应研究[J].数量经济技术经济研究,2016,33(6):41-58.(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.)
[4]刘亚军.互联网条件下的自发式包容性增长——基于一个“淘宝村”的纵向案例研究[J].社会科学,2017(10):46-60.(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.)
[5]成晨,丁冬.“互联网+农业电子商务”:现代农业信息化的发展路径[J].情报科学,2016,34(11):49-52,59.(Cheng Chen,Ding Dong.“Internet+Agricultural E-commerce”:Development Path of Modern Agricultural Informatization[J].Information Science,2016,34(11):49-52,59.)
[6]刘亚军,储新民.中国“淘宝村”的产业演化研究[J].中国软科学,2017(2):29-36.(Liu Yajun,Chu Xinmin.The Industrial Evolution of TAOBAO Villages[J].China Soft Science,2017(2):29-36.)
[7]王慧敏,王欣,李民.互联网+三农,探索农村电商新模式——以巢湖汤山电商村三瓜公社为例[J].农村经济与科技,2017,28(6):75-76,105.(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.
[10]Booth D E.Data Mining Methods and Models[J].Technometrics,2007,49(4):500.
[11]吴恒,陈燕翎.基于UGC文本挖掘的游客目的地选择信息研究——以携程蜜月游记为例[J].情报科学,2017,35(1):101-105.(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.)
[12]徐映梅,高一铭.基于互联网大数据的CPI舆情指数构建与应用——以百度指数为例[J].数量经济技术经济研究,2017(1):94-112.(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.)
[13]孟雪井,孟祥兰,胡杨洋.基于文本挖掘和百度指数的投资者情绪指数研究[J].宏观经济研究,2016(1):144-153.(Meng Xuejing,Meng Xianglan,Hu Yangyang.Research on Investor Sentiment Index Based on Text Mining and Baidu Index[J].Macroeconomics,2016(1):144-153.)
[14]孟雪井,杨亚飞,赵新泉.财经新闻与股市投资策略研究——基于财经网站的文本挖掘[J].投资研究,2016(8):29-37.(Meng Xuejing,Yang Yafei,Zhao Xinquan.The Research on Financial News and Stock Market Investment Strategy[J].Investment Research,2016(8):29-37.)
[15]吴联仁,李瑾颉,齐佳音.基于大规模文本数据情感挖掘的企业舆情研究[J].知识管理论坛,2016(6):457-463.(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,Mc Callum 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.
[19]史庆伟,李艳妮,郭朋亮.科技文献中作者研究兴趣动态发现[J].计算机应用,2013,33(11):3080-3083.(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.)
[20]范逢春.建国以来基本公共服务均等化政策的回顾与反思:基于文本分析的视角[J].上海行政学院学报,2016,17(1):46-57.(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.)
[21]张国兴,高秀林,汪应洛,等.中国节能减排政策的测量、协同与演变——基于1978-2013年政策数据的研究[J].中国人口·资源与环境,2014,24(12):62-73.(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.)
[22]张永宁,李辉,丛男,等.“情境-表达-结局”框架下中国减排政策变迁与反思——以“五年规划”为线索的文本挖掘[J].科技进步与对策,2016,33(20):109-114.(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.)
[23]张永安,闫瑾.基于文本挖掘的科技成果转化政策内部结构关系与宏观布局研究[J].情报杂志,2016,35(2):44-49.(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.)
[24]王印红,李萌竹.地方政府生态环境治理注意力研究——基于30个省市政府工作报告(2006—2015)文本分析[J].中国人口·资源与环境,2017,27(2):28-35.(Wang Yinhong,Li Mengzhu.Study on Local Government Attention of Ecological Environment Governance:Based on the Text Analysis of Government Work Report in 30 Provinces and Cities(2006—2015)[J].China Polulation,Resources and Environment,2017,27(2):28-35.)
[25]李华姣,张晗江,刘乃榕,等.我国资源环境承载力的时空分布特征及研究热点——基于在线新闻文本分析[J].资源与产业,2016,18(6):27-32.(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.)
[26]邵浩.贸易文本的主题挖掘研究[J].计算机工程与应用,2016,52(11):60-67.(Shao Hao.Topic Mining in Trade Policy Review[J].Computer Engineering and Applications,2016,52(11):60-67.)
[27]张永安,马昱.基于R语言的区域技术创新政策量化分析[J].情报杂志,2017,36(3):113-118.(Zhang Yong’an,Ma Yu.Quantitative Analysis of Regional Technological Innovation Policies with R Language[J].Journal of Intelligence,2017,36(3):113-118.)
[28]Python:An Interpreted High-level Programming Language for General-purpose Programming[EB/OL].[2017-10-11].https://www.python.org/doc/.
[29]单斌,李芳.基于LDA话题演化研究方法综述[J].中文信息学报,2010,24(6):43-49.(Shan Bin,Li Fang.A Survey of Topic Evolution Based on LDA[J].Journal of Chinese Information Processing,2010,24(6):43-49.)
[30]Blei D M.Latent Dirichlet Allocation[J].Journal of Machine Learning Research,2003,3:993-1022.
[2]吕岩威,刘洋.推动农村一二三产业融合发展的路径探究[J].当代经济管理,2017,39(10):38-43.(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.)
[3]鲁钊阳.新型农业经营主体发展的福利效应研究[J].数量经济技术经济研究,2016,33(6):41-58.(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.)
[4]刘亚军.互联网条件下的自发式包容性增长——基于一个“淘宝村”的纵向案例研究[J].社会科学,2017(10):46-60.(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.)
[5]成晨,丁冬.“互联网+农业电子商务”:现代农业信息化的发展路径[J].情报科学,2016,34(11):49-52,59.(Cheng Chen,Ding Dong.“Internet+Agricultural E-commerce”:Development Path of Modern Agricultural Informatization[J].Information Science,2016,34(11):49-52,59.)
[6]刘亚军,储新民.中国“淘宝村”的产业演化研究[J].中国软科学,2017(2):29-36.(Liu Yajun,Chu Xinmin.The Industrial Evolution of TAOBAO Villages[J].China Soft Science,2017(2):29-36.)
[7]王慧敏,王欣,李民.互联网+三农,探索农村电商新模式——以巢湖汤山电商村三瓜公社为例[J].农村经济与科技,2017,28(6):75-76,105.(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.
[10]Booth D E.Data Mining Methods and Models[J].Technometrics,2007,49(4):500.
[11]吴恒,陈燕翎.基于UGC文本挖掘的游客目的地选择信息研究——以携程蜜月游记为例[J].情报科学,2017,35(1):101-105.(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.)
[12]徐映梅,高一铭.基于互联网大数据的CPI舆情指数构建与应用——以百度指数为例[J].数量经济技术经济研究,2017(1):94-112.(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.)
[13]孟雪井,孟祥兰,胡杨洋.基于文本挖掘和百度指数的投资者情绪指数研究[J].宏观经济研究,2016(1):144-153.(Meng Xuejing,Meng Xianglan,Hu Yangyang.Research on Investor Sentiment Index Based on Text Mining and Baidu Index[J].Macroeconomics,2016(1):144-153.)
[14]孟雪井,杨亚飞,赵新泉.财经新闻与股市投资策略研究——基于财经网站的文本挖掘[J].投资研究,2016(8):29-37.(Meng Xuejing,Yang Yafei,Zhao Xinquan.The Research on Financial News and Stock Market Investment Strategy[J].Investment Research,2016(8):29-37.)
[15]吴联仁,李瑾颉,齐佳音.基于大规模文本数据情感挖掘的企业舆情研究[J].知识管理论坛,2016(6):457-463.(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,Mc Callum 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.
[19]史庆伟,李艳妮,郭朋亮.科技文献中作者研究兴趣动态发现[J].计算机应用,2013,33(11):3080-3083.(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.)
[20]范逢春.建国以来基本公共服务均等化政策的回顾与反思:基于文本分析的视角[J].上海行政学院学报,2016,17(1):46-57.(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.)
[21]张国兴,高秀林,汪应洛,等.中国节能减排政策的测量、协同与演变——基于1978-2013年政策数据的研究[J].中国人口·资源与环境,2014,24(12):62-73.(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.)
[22]张永宁,李辉,丛男,等.“情境-表达-结局”框架下中国减排政策变迁与反思——以“五年规划”为线索的文本挖掘[J].科技进步与对策,2016,33(20):109-114.(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.)
[23]张永安,闫瑾.基于文本挖掘的科技成果转化政策内部结构关系与宏观布局研究[J].情报杂志,2016,35(2):44-49.(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.)
[24]王印红,李萌竹.地方政府生态环境治理注意力研究——基于30个省市政府工作报告(2006—2015)文本分析[J].中国人口·资源与环境,2017,27(2):28-35.(Wang Yinhong,Li Mengzhu.Study on Local Government Attention of Ecological Environment Governance:Based on the Text Analysis of Government Work Report in 30 Provinces and Cities(2006—2015)[J].China Polulation,Resources and Environment,2017,27(2):28-35.)
[25]李华姣,张晗江,刘乃榕,等.我国资源环境承载力的时空分布特征及研究热点——基于在线新闻文本分析[J].资源与产业,2016,18(6):27-32.(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.)
[26]邵浩.贸易文本的主题挖掘研究[J].计算机工程与应用,2016,52(11):60-67.(Shao Hao.Topic Mining in Trade Policy Review[J].Computer Engineering and Applications,2016,52(11):60-67.)
[27]张永安,马昱.基于R语言的区域技术创新政策量化分析[J].情报杂志,2017,36(3):113-118.(Zhang Yong’an,Ma Yu.Quantitative Analysis of Regional Technological Innovation Policies with R Language[J].Journal of Intelligence,2017,36(3):113-118.)
[28]Python:An Interpreted High-level Programming Language for General-purpose Programming[EB/OL].[2017-10-11].https://www.python.org/doc/.
[29]单斌,李芳.基于LDA话题演化研究方法综述[J].中文信息学报,2010,24(6):43-49.(Shan Bin,Li Fang.A Survey of Topic Evolution Based on LDA[J].Journal of Chinese Information Processing,2010,24(6):43-49.)
[30]Blei D M.Latent Dirichlet Allocation[J].Journal of Machine Learning Research,2003,3:993-1022.