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基于文本数据挖掘技术的95598业务工单主题分析应用 被引量:6

The matic analysis and application of 95598 business order based on the techniques of text data mining
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摘要 运用LDA文档主题生成模型对海量95598业务工单进行文本挖掘,建立工单标签知识库,通过95598业务工单与标签知识库的识别匹配,形成样本工单。对样本工单开展主题分析,通过多维度数据对比,并结合专家经验和业务分析,提出专题策略建议,为客户提供精准化供电服务。 The article initially carries out a text mining on quantitative 95598 business orders by LDA text themes model and establishes knowledge base of order tags.The article further studies on the data match between 95598 business order and knowledge base of order tags so as to develop the exemplary order and thematic analysis.By comparing multi-dimension data and adopting expertise comments and business analysis,this article proposes specific strategies and suggestions so as to provide more precise supply services for customers.
出处 《电力需求侧管理》 2016年第A01期55-57,共3页 Power Demand Side Management
关键词 95598业务工单 文本挖掘 主题分析 95598 business order text mining thematic analysis
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  • 1张启蕊,张凌,董守斌,谭景华.训练集类别分布对文本分类的影响[J].清华大学学报(自然科学版),2005,45(S1):1802-1805. 被引量:27
  • 2曾雪强,王明文,陈素芬.一种基于潜在语义结构的文本分类模型[J].华南理工大学学报(自然科学版),2004,32(z1):99-102. 被引量:27
  • 3谌志群,张国煊.文本挖掘研究进展[J].模式识别与人工智能,2005,18(1):65-74. 被引量:49
  • 4苏金树,张博锋,徐昕.基于机器学习的文本分类技术研究进展[J].软件学报,2006,17(9):1848-1859. 被引量:387
  • 5G. Salton and M. McGill. Introduction to Modern Information Retrieval. McGraw-Hill, 1983.
  • 6S. Deerwester, S.Dumais T. Landauer, G. Furnas, and R. Harshman. Indexing by latent semantic analysis. Journal of the American Society of Information Science, 41(6):391-407, 1990.
  • 7S.T. Dumais. Latent semantic indexing (Isi): Trec-3 report. In Proceedings of the Text Retrieval Conference (TREC-3), 219-230, 1995.
  • 8T. Hofmann. Probabilistic latent semantic indexing. In Proceedings of the Twenty-Second Annual International SIGIR Conference, 50-57, 1999.
  • 9D.M. Blei, A.Y. Ng and M.I. Jordan. Latent, Dirichlet allocation. Journal of Machine Learning Research, 3:993-1022, 2003.
  • 10W.B. Frakes and R. Baeza-Yates, editors. Information Retrieval, Data Structure and Algorithms, Prentice Hall, ]992.

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