Email mining: tasks, common techniques, and tools

Tang, Guanting; Pei, Jian; Luk, Wo-Shun

doi:10.1007/s10115-013-0658-2

Cited by 56 publications

(23 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A number of email mining studies have focused on people-related tasks, including name recognition and reference resolution, contact information extraction, identity modeling and resolution, role discovery, and expert identification, as well as the generation of access to large-scale email archives from multiple viewpoints by using a faceted search [115]. Moreover, some significant applications of email mining include tasks, such as filtering emails based on priority and identifying spam and phishing emails, as well as automatic answering, thread summarization, contact analysis, email visualization, network property analysis, and categorization [116,117].…”

Section: Email Miningmentioning

confidence: 99%

Text Mining in Big Data Analytics

Hassani

Beneki

Unger

et al. 2020

BDCC

203

View full text Add to dashboard Cite

Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

show abstract

Section: Email Miningmentioning

confidence: 99%

Text Mining in Big Data Analytics

Hassani

Beneki

Unger

et al. 2020

BDCC

203

View full text Add to dashboard Cite

show abstract

“…For example, a sender wants to erase permanently and completely the message within a specified number of times, so that the sender does not need to worry about adverse effects as a result that the message will maintain long-term residence in system. Faced with the fact that various data mining techniques [12] have been applied on privacy data, it is obviously essential and necessary for a secure communication system that the sensitive data is timely destroyed to evade detection and forensic investigator.…”

Section: Forward Secrecy Encryptionmentioning

confidence: 99%

Secure Snaps: A New Forward Secrecy Cryptosystem for Self-Destructing Messages in Mobile Services

Zhu¹,

Yang²,

2015

2015 IEEE International Conference on Mobile Services

View full text Add to dashboard Cite

Recently, mobile services of ephemeral communications, such as Snapchat, are becoming increasingly popular because of their "burn after reading" functionality. However, recent events show that these services do not achieve the claimed functionality supporting truly self-destructing messages. In this paper, we address the problem of privacy protection in ephemeral communications involving untrusted service providers. We present SecureSnaps, a forward secrecy cryptosystem with self-destructing messages. In this system, we introduce a trusted module supervised by a trusted third party in order to ensure the security of user's private keys, so that untrusted service providers cannot access the communications among users. We take a new approach, called cryptographic counting control, to implement message's self-destruction functionality which meets the requirement for forward secrecy. By adopting this approach, no one, including the original sender, service provider, and key management module, can recover past messages once these messages have entered the "destroyed state". We prove that the proposed scheme is provably secure against chosen plaintext attacks. Our experiment results demonstrate its efficiency and practicality for large-size group of users.

show abstract

“…The content of business email is normally different from general text data in many documents. As pointed out by Tang et al [22], emails are often much shorter and more briefly written compared with documents such as stories and user manuals. In addition, emails often contain some faddish words or abbreviations that may not appear in traditional dictionaries.…”

Section: A Overivewmentioning

confidence: 99%

“…Over the past five decades, email has become one of the easiest and reliable modes of communication, mainly because of its efficiency, low cost and support for wide range of information [1]. Recent studies show that email is still number one online activity though there are new concepts like social networking [3].…”

Section: Introductionmentioning

confidence: 99%

Entity Extraction from Business Emails

Li¹,

Sen²,

Zaman³

2015

IJITCS

View full text Add to dashboard Cite

Email still plays an important role in today's business communication thanks to its simplicity, flexibility, low cost, and compatibility of diversified types of information. However processing the large amount of emails received consumes tremendous time and human power for a business. In order to quickly deciphering information and locate business-related information from emails received from a business, a computerized solution is required. In this paper, we have proposed a comprehensive mechanism to extract important information from emails. The proposed solution integrates semantic web technology with natural language processing and information retrieval. It enables automatic extraction of important entities from an email and makes batch processing of business emails efficient. The proposed mechanism has been used in a Transportation company.

show abstract

Email mining: tasks, common techniques, and tools

Cited by 56 publications

References 41 publications

Text Mining in Big Data Analytics

Text Mining in Big Data Analytics

Secure Snaps: A New Forward Secrecy Cryptosystem for Self-Destructing Messages in Mobile Services

Entity Extraction from Business Emails

Contact Info

Product

Resources

About