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A novel approach for Arabic business email classification based on deep learning machines

During the last decades, the reliance on email communication, especially in business, has increased significantly. Companies receive a massive amount of emails daily, that include business inquiries, customers’ feedback, and other types of emails. This inspired many researchers to propose different...

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Detalles Bibliográficos
Autores principales: Masri, Aladdin, Al-Jabi, Muhannad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280450/
https://www.ncbi.nlm.nih.gov/pubmed/37346608
http://dx.doi.org/10.7717/peerj-cs.1221
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author Masri, Aladdin
Al-Jabi, Muhannad
author_facet Masri, Aladdin
Al-Jabi, Muhannad
author_sort Masri, Aladdin
collection PubMed
description During the last decades, the reliance on email communication, especially in business, has increased significantly. Companies receive a massive amount of emails daily, that include business inquiries, customers’ feedback, and other types of emails. This inspired many researchers to propose different algorithms to classify and redistribute the numerous emails according to their content. Nowadays, emails containing Arabic text, especially in the Arab world, have raised an increasing concern since they became widely used in official correspondence. Nevertheless, just a small amount of literature focuses on Arabic text classification. Therefore, this work addresses Arabic business emails classification based on natural language processing (NLP). A dataset of 63,257 emails was used and the emails were classified as: urgency, sentiment, and topic classification. The proposed models are based on machine learning techniques and a lexicon of words on which the emails are identified. The models are composed of different settings of convolutional neural networks (CNN). A separate model was built, trained, and tested for each category. The results were promising and gave an accuracy of about 92% and a loss of less than 8%. They also proved the correctness and robustness of this work.
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spelling pubmed-102804502023-06-21 A novel approach for Arabic business email classification based on deep learning machines Masri, Aladdin Al-Jabi, Muhannad PeerJ Comput Sci Data Mining and Machine Learning During the last decades, the reliance on email communication, especially in business, has increased significantly. Companies receive a massive amount of emails daily, that include business inquiries, customers’ feedback, and other types of emails. This inspired many researchers to propose different algorithms to classify and redistribute the numerous emails according to their content. Nowadays, emails containing Arabic text, especially in the Arab world, have raised an increasing concern since they became widely used in official correspondence. Nevertheless, just a small amount of literature focuses on Arabic text classification. Therefore, this work addresses Arabic business emails classification based on natural language processing (NLP). A dataset of 63,257 emails was used and the emails were classified as: urgency, sentiment, and topic classification. The proposed models are based on machine learning techniques and a lexicon of words on which the emails are identified. The models are composed of different settings of convolutional neural networks (CNN). A separate model was built, trained, and tested for each category. The results were promising and gave an accuracy of about 92% and a loss of less than 8%. They also proved the correctness and robustness of this work. PeerJ Inc. 2023-01-25 /pmc/articles/PMC10280450/ /pubmed/37346608 http://dx.doi.org/10.7717/peerj-cs.1221 Text en ©2023 Masri and Al-Jabi https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Data Mining and Machine Learning
Masri, Aladdin
Al-Jabi, Muhannad
A novel approach for Arabic business email classification based on deep learning machines
title A novel approach for Arabic business email classification based on deep learning machines
title_full A novel approach for Arabic business email classification based on deep learning machines
title_fullStr A novel approach for Arabic business email classification based on deep learning machines
title_full_unstemmed A novel approach for Arabic business email classification based on deep learning machines
title_short A novel approach for Arabic business email classification based on deep learning machines
title_sort novel approach for arabic business email classification based on deep learning machines
topic Data Mining and Machine Learning
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280450/
https://www.ncbi.nlm.nih.gov/pubmed/37346608
http://dx.doi.org/10.7717/peerj-cs.1221
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