Arias-Barahona María Ximena, Arteaga-Arteaga Harold Brayan, Orozco-Arias Simón, Flórez-Ruíz Juan Camilo, Valencia-Díaz Mario Andrés, Tabares-Soto Reinel
Department of Electronics and Automation, Universidad Autónoma de Manizales, Manizales, Caldas, Colombia.
Department of Computer Science, Universidad Autónoma de Manizales, Manizales, Caldas, Colombia.
PeerJ Comput Sci. 2023 Mar 17;9:e1016. doi: 10.7717/peerj-cs.1016. eCollection 2023.
Artificial intelligence (AI) is one of the components recognized for its potential to transform the way we live today radically. It makes it possible for machines to learn from experience, adjust to new contributions and perform tasks like human beings. The business field is the focus of this research. This article proposes implementing an incident classification model using machine learning (ML) and natural language processing (NLP). The application is for the technical support area in a software development company that currently resolves customer requests manually. Through ML and NLP techniques applied to company data, it is possible to know the category of a request given by the client. It increases customer satisfaction by reviewing historical records to analyze their behavior and correctly provide the expected solution to the incidents presented. Also, this practice would reduce the cost and time spent on relationship management with the potential consumer. This work evaluates different Machine Learning models, such as support vector machine (SVM), Extra Trees, and Random Forest. The SVM algorithm demonstrates the highest accuracy of 98.97% with class balance, hyper-parameter optimization, and pre-processing techniques.
人工智能（AI）是被公认为有潜力从根本上改变我们当今生活方式的要素之一。它使机器能够从经验中学习，适应新的输入并像人类一样执行任务。商业领域是本研究的重点。本文提出使用机器学习（ML）和自然语言处理（NLP）来实现一个事件分类模型。该应用针对的是一家软件开发公司的技术支持领域，该领域目前手动处理客户请求。通过将ML和NLP技术应用于公司数据，可以了解客户提出请求的类别。通过查看历史记录来分析客户行为并正确提供针对所出现事件的预期解决方案，从而提高客户满意度。此外，这种做法将减少与潜在消费者进行关系管理所花费的成本和时间。这项工作评估了不同的机器学习模型，如支持向量机（SVM）、极端随机树和随机森林。SVM算法在类别平衡、超参数优化和预处理技术的情况下展示了98.97%的最高准确率。
