Work Hard, Play Hard: Email Classification on the Avocado and Enron Corpora
In this paper, we present an empirical study of email classification into two main categories {`}Business{''} and {`}Personal{''}. We train on the Enron email corpus, and test on the Enron and Avocado email corpora. We show that information from the email exchange networks improves the performance of classification. We represent the email exchange networks as social networks with graph structures. For this classification task, we extract social networks features from the graphs in addition to lexical features from email content and we compare the performance of SVM and Extra-Trees classifiers using these features. Combining graph features with lexical features improves the performance on both classifiers. We also provide manually annotated sets of the Avocado and Enron email corpora as a supplementary contribution.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationGeneral ClassificationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Learning with Weak Supervision for Email Intent Detection
Email remains one of the most frequently used means of online communication. People spend a significant amount of time every day on emails to exchange information, manage tasks and schedule events. Previous work has stud…
intent-classificationIntent ClassificationIntent DetectionSchedulingDebate-Driven Multi-Agent LLMs for Phishing Email Detection
Phishing attacks remain a critical cybersecurity threat. Attackers constantly refine their methods, making phishing emails harder to detect. Traditional detection methods, including rule-based systems and supervised mach…
Language ModelingLanguage ModellingLarge Language ModelPhishing Email Detection Using Large Language Models
Email phishing is one of the most prevalent and globally consequential vectors of cyber intrusion. As systems increasingly deploy Large Language Models (LLMs) applications, these systems face evolving phishing email thre…
Adversarial AttackA Three-Player GAN: Generating Hard Samples To Improve Classification Networks
We propose a Three-Player Generative Adversarial Network to improve classification networks. In addition to the game played between the discriminator and generator, a competition is introduced between the generator and t…
ClassificationGeneral ClassificationGenerative Adversarial NetworkTraffic Sign RecognitionPrivacy-Preserving Spam Filtering using Functional Encryption
Traditional spam classification requires the end-user to reveal the content of its received email to the spam classifier which violates the privacy. Spam classification over encrypted emails enables the classifier to cla…
ClassificationGeneral ClassificationPrivacy Preserving