基于自然语言处理技术的电力客服工单文本挖掘策略

Houen Li, Zhicheng Li, Zhuyi Rao
{"title":"基于自然语言处理技术的电力客服工单文本挖掘策略","authors":"Houen Li, Zhicheng Li, Zhuyi Rao","doi":"10.1109/ICICAS48597.2019.00078","DOIUrl":null,"url":null,"abstract":"In order to improve the level of power customer service, we need to start with natural language processing technology, and conduct in-depth text mining of power customer complaint work order. Combined with the format and characteristics of power customer service work order, this paper proposed a text mining strategy of power customer service work order based on natural language processing technology which includes the following processes: work order data cleaning, text segmentation, information characterization, model training and model evaluation is proposed. The TF-IDF algorithm and the model optimization method based on accuracy calculation are applied in this strategy, which greatly improves the level of power customer service.","PeriodicalId":409693,"journal":{"name":"2019 International Conference on Intelligent Computing, Automation and Systems (ICICAS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Text Mining Strategy of Power Customer Service Work Order Based on Natural Language Processing Technology\",\"authors\":\"Houen Li, Zhicheng Li, Zhuyi Rao\",\"doi\":\"10.1109/ICICAS48597.2019.00078\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to improve the level of power customer service, we need to start with natural language processing technology, and conduct in-depth text mining of power customer complaint work order. Combined with the format and characteristics of power customer service work order, this paper proposed a text mining strategy of power customer service work order based on natural language processing technology which includes the following processes: work order data cleaning, text segmentation, information characterization, model training and model evaluation is proposed. The TF-IDF algorithm and the model optimization method based on accuracy calculation are applied in this strategy, which greatly improves the level of power customer service.\",\"PeriodicalId\":409693,\"journal\":{\"name\":\"2019 International Conference on Intelligent Computing, Automation and Systems (ICICAS)\",\"volume\":\"44 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 International Conference on Intelligent Computing, Automation and Systems (ICICAS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICICAS48597.2019.00078\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Intelligent Computing, Automation and Systems (ICICAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICAS48597.2019.00078","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

为了提高电力客户服务水平,需要从自然语言处理技术入手,对电力客户投诉工单进行深入的文本挖掘。结合电力客服工单的格式和特点,提出了一种基于自然语言处理技术的电力客服工单文本挖掘策略,该策略包括工单数据清洗、文本分割、信息刻画、模型训练和模型评价等流程。该策略采用TF-IDF算法和基于精度计算的模型优化方法,大大提高了电力客户服务水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Text Mining Strategy of Power Customer Service Work Order Based on Natural Language Processing Technology
In order to improve the level of power customer service, we need to start with natural language processing technology, and conduct in-depth text mining of power customer complaint work order. Combined with the format and characteristics of power customer service work order, this paper proposed a text mining strategy of power customer service work order based on natural language processing technology which includes the following processes: work order data cleaning, text segmentation, information characterization, model training and model evaluation is proposed. The TF-IDF algorithm and the model optimization method based on accuracy calculation are applied in this strategy, which greatly improves the level of power customer service.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信