对于使用机器学习和自然语言处理的软件公司,要求在客户服务领域进行分类.
María Ximena Arias-Barahona1, Harold Brayan Arteaga-Arteaga1, Simón Orozco-Arias2,3
1Department of Electronics and Automation, Universidad Autónoma de Manizales, Manizales, Caldas, Colombia.
PeerJ. Computer science
|June 22, 2023
概括
本研究介绍了一种基于人工智能的事件分类模型,用于软件技术支持. 利用机器学习 (ML) 和自然语言处理 (NLP),它自动化了请求分类,提高了客户满意度和运营效率.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 商业部门越来越多地采用人工智能进行变革性应用.
- 在技术支持中手动解决客户请求是耗时和昂贵的.
- 自动化事件分类可以提高客户满意度并降低运营费用.
研究的目的:
- 为软件开发公司的技术支持提出和评估事件分类模型.
- 利用机器学习 (ML) 和自然语言处理 (NLP) 来自动化请求分类.
- 提高客户满意度,降低与手动支持相关的成本.
主要方法:
- 使用ML和NLP技术实施事件分类模型.
- 分析公司历史数据以训练和测试模型.
- 评估各种ML算法,包括支持矢量机 (SVM),额外树木和随机森林.
主要成果:
- 支持矢量机 (SVM) 算法实现了最高准确率的98.97%.
- 通过类平衡,超参数调整和预处理技术优化了模型性能.
- 拟议的模型有效地对客户请求进行分类,使得客户行为的数据驱动分析成为可能.
结论:
- 人工智能驱动的事件分类显著提高了技术支持的效率.
- 自动化分类导致更好的资源配置和更快的解析时间.
- 开发的ML/NLP模型为软件公司的客户事件管理提供了可扩展和准确的解决方案.
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