SankeyVis: Visualizing active relationship from emails based on multiple dimensions and topic classification methods

Fang, Yong; Zhao, Cuirong; Huang, Cheng; Liu, Liang

doi:10.1016/j.fsidi.2020.300981

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…Due to the increasing number of email messages and the importance of communication worldwide, several aspects of email communication have the attention of many researchers. In Fang et al (2020) , the authors focused on forensics analysis by introducing a visualization model for email forensics of active relations aimed at mining social relationships and semantic patterns in emails. Other researchers focused on detecting attacks against systems.…”

Section: Literature Reviewmentioning

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: Literature Reviewmentioning

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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Towards privacy-aware exploration of archived personal emails

Bartliff,

Kim,

Hopfgartner

2024

Int J Digit Libr

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This paper examines how privacy measures, such as anonymisation and aggregation processes for email collections, can affect the perceived usefulness of email visualisations for research, especially in the humanities and social sciences. The work is intended to inform archivists and data managers who are faced with the challenge of accessioning and reviewing increasingly sizeable and complex personal digital collections. The research in this paper provides a focused user study to investigate the usefulness of data visualisation as a mediator between privacy-aware management of data and maximisation of research value of data. The research is carried out with researchers and archivists with vested interest in using, making sense of, and/or archiving the data to derive meaningful results. Participants tend to perceive email visualisations as useful, with an average rating of 4.281 (out of 7) for all the visualisations in the study, with above average ratings for mountain graphs and word trees. The study shows that while participants voice a strong desire for information identifying individuals in email data, they perceive visualisations as almost equally useful for their research and/or work when aggregation is employed in addition to anonymisation.

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SankeyVis: Visualizing active relationship from emails based on multiple dimensions and topic classification methods

Cited by 2 publications

References 27 publications

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

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

Towards privacy-aware exploration of archived personal emails

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