基于GRW和FastText模型的电信用户投诉文本分类应用

赵进; 杨小军

doi:10.11959/j.issn.1000-0801.2021125

您当前的位置：

首页 >

文章列表页 >

基于GRW和FastText模型的电信用户投诉文本分类应用

研究与开发 | 更新时间：2024-06-05

- 基于GRW和FastText模型的电信用户投诉文本分类应用
- Application on text classification of telecom user complaints based on GRW and FastText model
- 电信科学 2021年37卷第6期页码：125-131
- 作者机构：
- 作者简介：
  
  [ "赵进（1989- ），男，现就职于中国电信股份有限公司重庆分公司，主要研究方向为大数据、人工智能、开源软件等" ]
  [ "杨小军（1970- ），男，现就职于中国电信股份有限公司重庆分公司，主要研究方向为云计算、大数据、人工智能、开源软件等" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2021125
  中图分类号： TP391.1
- 网络出版日期：2021-06，
  
  纸质出版日期：2021-06-20
- 稿件说明：
移动端阅览
赵进, 杨小军. 基于GRW和FastText模型的电信用户投诉文本分类应用[J]. 电信科学, 2021,37(6):125-131.

Jin ZHAO, Xiaojun YANG. Application on text classification of telecom user complaints based on GRW and FastText model[J]. Telecommunications science, 2021, 37(6): 125-131.
赵进, 杨小军. 基于GRW和FastText模型的电信用户投诉文本分类应用[J]. 电信科学, 2021,37(6):125-131. DOI： 10.11959/j.issn.1000-0801.2021125.

Jin ZHAO, Xiaojun YANG. Application on text classification of telecom user complaints based on GRW and FastText model[J]. Telecommunications science, 2021, 37(6): 125-131. DOI： 10.11959/j.issn.1000-0801.2021125.

摘要

随着神经网络的广泛应用，将神经网络应用到自然语言处理文本分类问题中，成为一种有效的解决方法。电信运营商客户服务中心通过多种渠道收集用户投诉信息，为了对投诉文本信息进行自动分类并将其落实到具体责任部门，提升用户感知，提出了一种基于GRW模型和FastText模型的文本分类方法。首先通过GRW模型对投诉文本进行特征选择，提取有效特征词；然后构建基于FastText模型的用户投诉文本分类方法；最后在公开数据集和运营商已标注的投诉文本数据集上进行实验。结果表明，基于 GRW 和 FastText模型的文本分类方法比朴素贝叶斯、双向LSTM和Bert模型在准确率、Kappa系数及汉明损失方面的性能有较大提升。

Abstract

With the widespread application of neural network

the application of neural network to natural language processing text classification problems has become an effective solution.The customer service center of telecom operator collected user complaint information from multiple channels.In order to automatically classify the complaint text information and assign it to the specific responsible department for processing and reply

enhancing customer perception further

a textclassification method based on GRW and FastTextmodel was proposed.Firstly

the GRW model was used to select the features of the complaint text

extract effective feature words

and then a user complaint text classification method based on FastText model was constructed.Experiments on public datasets and a complaint text data by one of telecom company show that the text classification method based on GRW and FastText model is better than naive Bayes

bidirectional LSTM and Bert pre-trained model in accuracy

Kappa coefficient and Hamming loss.

关键词

Keywords

references

FAED A , CHANG E , SABERI M , et al . Intelligent customer complaint handling utilising principal component and data envelopment analysis (PDA) [J ] . Applied Soft Computing , 2016 , 47 : 614 - 630 .

梁昕露 , 李美娟 . 电信业投诉分类方法及其应用研究 [J ] . 中国管理科学 , 2015 , 23 ( S1 ): 188 - 192 .

LIANG X L , LI M J . Text categorization of complain in telecommunication industry and its applied research [J ] . Chinese Journal of Management Science , 2015 , 23 ( S1 ): 188 - 192 .

Handbook of natural language processing [Z ] . 2010 .

李丹 . 基于朴素贝叶斯方法的中文文本分类研究 [D ] . 保定:河北大学 , 2011 .

LI D . The study of Chinese text categorization based on Naive Bayes [D ] . Baoding:Hebei University , 2011 .

LAFFERTY J D , MCCALLUM A , PEREIRA F C N . Conditional random fields:probabilistic models for segmenting and labeling sequence data [C ] // Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001) .[S.l.:s.n. ] , 2001 .

KALAIVANI K S , KUPPUSWAMI S . Exploring the use of syntactic dependency features for document-level sentiment classification [J ] . Bulletin of the Polish Academy of Sciences.Technical Sciences , 2019 , 67 ( 2 ).

IMAMBI S S , SUDHA T . A novel feature selection method for classification of medical documents from pubmed [J ] . International Journal of Computer Applications , 2011 , 26 ( 9 ): 29 - 33 .

JOULIN A , GRAVE E , BOJANOWSKI P , et al . Bag of tricks for efficient text classification [C ] // Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics .[S.l.:s.n. ] , 2017 : 427 - 431 .

KRAEMER H C . Kappa coefficient [J ] . Wiley StatsRef:Statistics Reference Online , 2014 : 1 - 4 .

GAO W , ZHOU Z H . On the consistency of multi-label learning [J ] . Journal of Machine Learning Research , 2011 ( 19 ): 341 - 358 .

浏览量

321

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于深度学习的文本分类系统关键技术研究与模型验证

基于Transformer模型的社交网络影响力最大化算法

多模型融合的客服工单文本分类方法的研究与实现

基于多层感知机模型的天线方位角诊断

基于机器学习的光纤窃听检测方法