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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2023-01-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1221.pdf |
| _version_ | 1828052798300749824 |
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| author | Aladdin Masri Muhannad Al-Jabi |
| author_facet | Aladdin Masri Muhannad Al-Jabi |
| author_sort | Aladdin Masri |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-10T19:57:22Z |
| format | Article |
| id | doaj.art-a51252b175df4429b15561f6f41d24df |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-04-10T19:57:22Z |
| publishDate | 2023-01-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-a51252b175df4429b15561f6f41d24df2023-01-27T15:05:28ZengPeerJ Inc.PeerJ Computer Science2376-59922023-01-019e122110.7717/peerj-cs.1221A novel approach for Arabic business email classification based on deep learning machinesAladdin Masri0Muhannad Al-Jabi1Computer Engineering Department, An-Najah National University, Nablus, PalestineComputer Engineering Department, An-Najah National University, Nablus, PalestineDuring 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.https://peerj.com/articles/cs-1221.pdfMachine learningEmail classificationNatural language processingArabic lexicon |
| spellingShingle | Aladdin Masri Muhannad Al-Jabi A novel approach for Arabic business email classification based on deep learning machines PeerJ Computer Science Machine learning Email classification Natural language processing Arabic lexicon |
| 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 | Machine learning Email classification Natural language processing Arabic lexicon |
| url | https://peerj.com/articles/cs-1221.pdf |
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