A Novel Approach for Enhancing Vietnamese Sentiment Classification

Nguyen, Cuong V.; Le, Khiem H.; Nguyen, Binh T.

doi:10.1007/978-3-030-79463-7_9

Cited by 5 publications

(1 citation statement)

References 21 publications

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“…Deep learning natural language processing (NLP) has become very popular due to its capability to handle text, even if it is far from being grammatically correct. Machine learning techniques have been used to get modern results on NLP tasks like text classification, text ranking, question answering, relation classification, text summarization, machine translation, and others ( Joshi, Goel & Joshi, 2020 ; Bianchi, Nozza & Hovy, 2021 ; Nguyen, Le & Nguyen, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

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

Masri

Al-Jabi

2023

PeerJ Computer Science

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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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Section: Introductionmentioning

confidence: 99%