Requests classification in the customer service area for software companies using machine learning and natural language processing
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 resear...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2023-03-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1016.pdf |
| _version_ | 1797867305491759104 |
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| author | María Ximena Arias-Barahona Harold Brayan Arteaga-Arteaga Simón Orozco-Arias Juan Camilo Flórez-Ruíz Mario Andrés Valencia-Díaz Reinel Tabares-Soto |
| author_facet | María Ximena Arias-Barahona Harold Brayan Arteaga-Arteaga Simón Orozco-Arias Juan Camilo Flórez-Ruíz Mario Andrés Valencia-Díaz Reinel Tabares-Soto |
| author_sort | María Ximena Arias-Barahona |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T23:39:14Z |
| format | Article |
| id | doaj.art-42ae96fd9d9744e9b74f6d8b90e2b462 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-04-09T23:39:14Z |
| publishDate | 2023-03-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-42ae96fd9d9744e9b74f6d8b90e2b4622023-03-19T15:05:04ZengPeerJ Inc.PeerJ Computer Science2376-59922023-03-019e101610.7717/peerj-cs.1016Requests classification in the customer service area for software companies using machine learning and natural language processingMaría Ximena Arias-Barahona0Harold Brayan Arteaga-Arteaga1Simón Orozco-Arias2Juan Camilo Flórez-Ruíz3Mario Andrés Valencia-Díaz4Reinel Tabares-Soto5Department of Electronics and Automation, Universidad Autónoma de Manizales, Manizales, Caldas, ColombiaDepartment of Electronics and Automation, Universidad Autónoma de Manizales, Manizales, Caldas, ColombiaDepartment of Computer Science, Universidad Autónoma de Manizales, Manizales, Caldas, ColombiaDepartment of Electronics and Automation, Universidad Autónoma de Manizales, Manizales, Caldas, ColombiaSIGMA Ingeniería S.A, Manizales, Caldas, ColombiaDepartment of Electronics and Automation, Universidad Autónoma de Manizales, Manizales, Caldas, ColombiaArtificial 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.https://peerj.com/articles/cs-1016.pdfNatural language processingMachine learningConsumer serviceRequests classificationText classification |
| spellingShingle | María Ximena Arias-Barahona Harold Brayan Arteaga-Arteaga Simón Orozco-Arias Juan Camilo Flórez-Ruíz Mario Andrés Valencia-Díaz Reinel Tabares-Soto Requests classification in the customer service area for software companies using machine learning and natural language processing PeerJ Computer Science Natural language processing Machine learning Consumer service Requests classification Text classification |
| title | Requests classification in the customer service area for software companies using machine learning and natural language processing |
| title_full | Requests classification in the customer service area for software companies using machine learning and natural language processing |
| title_fullStr | Requests classification in the customer service area for software companies using machine learning and natural language processing |
| title_full_unstemmed | Requests classification in the customer service area for software companies using machine learning and natural language processing |
| title_short | Requests classification in the customer service area for software companies using machine learning and natural language processing |
| title_sort | requests classification in the customer service area for software companies using machine learning and natural language processing |
| topic | Natural language processing Machine learning Consumer service Requests classification Text classification |
| url | https://peerj.com/articles/cs-1016.pdf |
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