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About 4,416 papers for “email classification”

Classification of eMail Communication

Piják, Marek · Brno University of Technology Digital Library (Brno University of Technology) · 2018

Tato diplomová práce se zabývá vytvořením klasifikátoru, který bude schopen rozpoznat emailovou zprávu společnosti Topefekt.s.r.o a zařadit ji do korespondující klasifikační třídy. Tento projekt bude využívat řadu nejpoužívanějších klasifikačních metod včetně strojového učení. Jako součást této práce bude i ohodnocení úspěšnosti jednotlivých metod.

Cited by 0

Email classification for contact centers

Ani Nenkova, Amit Bagga · Proceedings of the 2003 ACM symposium on Applied computing - SAC '03 · 2003

The explosive growth of the Internet has made email an integral part of business communication. Therefore, business customer service centers, or contact centers, are processing larger amounts of email interactions with customers. In this paper we discuss a preliminary email routing and classification system that filters and classifies incoming email messages upon their content. A module first attempts to identify and filter those email messages that do not require immediate (if any) responses. We call such email messages single messages. The emails that do require immediate responses are called root messages. A second module classifies messages in categories that characterize the type of interaction between the contact center operators and the customers. Emails that are involved in such interactions form a thread and can be classified broadly into one of three categories: root, inner, and leaf. Root messages are those that start a thread while a leaf message is the final email sent in an interaction. All other emails in the interaction are considered to be inner messages.

Cited by 1

An Intelligent Email Classification System

Zili Luo, Farhana Zulkernine · 2023

Email is one of the most common methods of official and personal communication to exchange information. For the administration department, dealing with hundreds of emails with the same type of inquiries or requests results in a huge operational overhead. In this study, we explore email classification models. An email classification system should understand the topics in the email content for categorizing emails and indicate if an incoming email should be handled by the mailbox owner. Email categorization based on topics is a multi-label classification task. Most existing email categorization models perform binary classification to identify spam, phishing, or malware attacks. We propose a CNN-BiLSTM model for multiclass email classification. Our experiments show that compared to the two other models that we implemented namely CNN (76.19%) and BiLSTM (61.9%) models, the CNN-BiLSTM (83.33%) and Hierarchical CNN-BiLSTM models (85.33%) have much better performance.

Cited by 1

Email Spam Classification Using LBSVM

Lehan Zhang, Xiaoyu Jiang, Qinyuan Zheng et al. · 2024

Spam classification is a popular topic in modern information security and personal privacy. It not only occupies network resources, leads to network congestion, but also causes economic losses for people. Due to the development of artificial intelligence and information processing technology, content statistics technology based on machine learning and deep learning has greatly improved the efficiency of spam classification. This article focuses on the topic of spam, and we propose the “Spam-7 Category”, which creatively classifies spam into advertising emails, scanning emails, phishing emails, emails spreading malware, pornographic emails, chain emails, and false warnings or notifications emails. We propose the Beijing Jiaotong University Email Spam (BJES) Datasets datasets to address the shortage of Chinese spam datasets. On the basis of studying past spam detection techniques, we propose the LBSVM model (a neural network model based on SVM and BERT) for spam classification tasks. LBSVM achieved accuracy of 98.3% and 90.1 % on the trec06c and BJES datasets, respectively. It was also experimentally compared with Naive Bayes (Gaussian NB), Naive Bayes(MultinomiaINB),SVM, and Logistic Regression algorithms to demonstrate its feasibility and efficiency in spam classification tasks.

Cited by 0

Semantic classification of email Data

2012

The data is comprised of a pool of 1 million emails from the Enron Corporation that were made available to the public. From these, N=1805 emails were tagged with semantic labels. The labels are in the domain of meetings. Each email can have multiple labels; this makes the classification problem more interesting, the idea is to try to retrieve correctly all the labels assigned to a particular email. Most of the emails transcribed are labeled else, leaving relatively few classes with enough training samples to produce significant results. attend addagenda addagendaitems addattendees cancelactive cancelpassive else organize preparematerials removeattendees

Cited by 0

Classification of Email Using ANN

Hareb Ghaydaa, Ahmed Alghoul, Ajrami Ghada et al. · PhilPapers (PhilPapers Foundation) · 2016

In recent years email has become one of the fastest and most economical means of communication. However increase of email users has resulted in the dramatic increase of spam emails during the past few years. Data mining -classification algorithms are used to categorize the email as spam or non-spam. Numerous email spam messages are marketable in nature but might similarly encompass camouflaged links that seem to be for acquainted websites but actually lead to phishing web sites or sites that are holding malware. Spam email might likewise comprise malware as scripts or other executable file attachments. Spammers use spam bots to generate email distribution lists. A spammer naturally sends an email to millions of email addresses. The address and identity of the sender are concealed Mass Mailing with the expectation that only a small number will respond or interact with the message and Spam mails might be the cause of phishing attack, hacking of banking accounts, attacks on confidential data. Spamming is rising at a quick speed as sending a deluge of mails is simple and for free. Spam mails interrupt the one calmness, spending much time and put away various resources like memory and networks bandwidth. In this study, we present a method for spam filtering using Artificial Neural Network to predict whether an email is spam or not.

Cited by 0

Email Spam Classification using DistilBERT

Vance I. Del Rosario, Benjamin David P. Fernandez, Dionis A. Padilla · 2023

The increase of worldwide email users generated an estimate of 376.4 billion emails sent in a day. 85% of the sent emails were spam. To classify and filter increasing number of emails, Machine learning and Artificial Intelligence is applied. Distilled versions of transformer models for classification arise to perform on par with existing transformer models. It aims to be faster ang lighter. The objective of the study is to create a standalone device that identifies spam and ham using DistilBERT. The model is trained in several datasets with 11,214 ham and 7,287 spam emails. The device run in a flask server of a Raspberry Pi. It is inferenced with 100 spam and 100 ham emails with 95 spam and 97 ham emails correctly classified. The accuracy of the device is 95.99% with a MCC of 92.02%. It gained a positive correlation and prediction. Further areas of research are the improvement of training the model.

Cited by 3

Email Classification Using Artificial Neural Network

Ahmed Alghoul; Sara Al Ajrami; Ghada Al Jarousha; Samy S. Abu-Naser · Zenodo (CERN European Organization for Nuclear Research) · 2026

International Journal of Academic Engineering Research

Cited by 0Open access

Email classification with limited labeled data

Peter Engström · Doria (University of Helsinki) · 2020

In this thesis I evaluate different ways of classifying email messages in the absence of a large number of pre-classified training messages. When using machine learning to classify incoming emails, this lack of pre-labeled emails is problematic. These labels can only be acquired by going through each email and manually labeling it, which for a large number of emails can take very long. There are existing studies on machine learning for email classification, but the amount of labeled data available for such studies is generally large. However, there have been studies of non-email text classification with smaller amounts of labeled data, namely in the field of semi-supervised learning. I experiment with supervised and semi-supervised (specifically, self-training) algorithms. The supervised methods used are random forest, naïve Bayes, support-vector machine, and AdaBoost. The experiments are conducted using 1000 labeled training emails. Three classes are used for the classification of data. Class “DB” represents the group that processes emails involving database stored procedures; class “CIM”, emails involving the Customer Interaction Management system; and class “Integration”, emails involving data integration. The results show that self-training offers no significant advantage over the supervised methods for this task. Of the supervised methods, the support-vector machine achieves the best performance for each of the three classes.

Cited by 1

Clustering and classification of email contents

Izzat Alsmadi, Ikdam Alhami · Journal of King Saud University - Computer and Information Sciences · 2015

Information users depend heavily on emails’ system as one of the major sources of communication. Its importance and usage are continuously growing despite the evolution of mobile applications, social networks, etc. Emails are used on both the personal and professional levels. They can be considered as official documents in communication among users. Emails’ data mining and analysis can be conducted for several purposes such as: Spam detection and classification, subject classification, etc. In this paper, a large set of personal emails is used for the purpose of folder and subject classifications. Algorithms are developed to perform clustering and classification for this large text collection. Classification based on NGram is shown to be the best for such large text collection especially as text is Bi-language (i.e. with English and Arabic content).

Cited by 105Open access