Real Email Data Research Articles

Sequence encoding incorporated CNN model for Email document sentiment classification

Document sentiment classification is an area of study that has been developed for decades. However, sentiment classification of Email data is rather a specialized field that has not yet been thoroughly studied. Compared to typical social media and review data, Email data has characteristics of length variance, duplication caused by reply and forward messages, and implicitness in sentiment indicators. Due to these characteristics, existing techniques are incapable of fully capturing the complex syntactic and relational structure among words and phrases in Email documents.In this study, we introduce a dependency graph-based position encoding technique enhanced with weighted sentiment features, and incorporate it into the feature representation process. We combine encoded sentiment sequence features with traditional word embedding features as input for a revised deep CNN model for Email sentiment classification. Experiments are conducted on three sets of real Email data with adequate label conversion processes. Empirical results indicate that our proposed SSE-CNN model obtained the highest accuracy rate of 88.6%, 74.3% and 82.1% for three experimental Email datasets over other comparative state-of-the-art algorithms. Furthermore, our performance evaluations on the preprocessing and sentiment sequence encoding justify the effectiveness of Email preprocessing and sentiment sequence encoding with dependency-graph based position and SWN features on the improvement of Email document sentiment classification.

Discovering sentiment sequence within email data through trajectory representation

Traditional document-level sentiment analysis fails to consider sentiment sequence within documents. This research paper proposes a novel perspective of sequence-based document sentiment analysis for discovering sentiment sequence and clustering sentiments for Email data. The proposed scheme of approach applies a trajectory clustering algorithm to Email trajectories transformed from sentiment features generated from SentiWordNet lexicon for discovering sentiment sequence within topic and temporal pattern distributions on the basis of trajectory clusters and their representative trajectories. Experiments conducted on real Email data provide evidence on proving the feasibility of the proposed technique and justifying the indispensability of sentiment sequence within documents in the determination of sentiment polarity.

Email Classification Using Behavior and Time Features

The various forms and tremendous number of spam emails have brought great challenges to accurate email classification. In this paper, we present a behavior- and time-feature-based email classification method. Based on email logs, email social networks are built through the extraction of entities and relations from the email records using the MapReduce model. By combining behavior features from social networks and time features from email sending intervals, we adopt a Support Vector Machine based classifier to identify spammers and nonspammers. Compared with the current email classification methods, the advantages of our method are: (1) in addition to the behavior-based features, our method integrates the time feature to facilitate email classification; (2) to efficiently handle the vast number of emails, we employ the MapReduce model to extract the behavior- and time-based features on the email social network. Experiments on real email data of three years show that the proposed method achieves better classification accuracy.

Disclosing user relationships in email networks

To reveal patterns of communications of users in a network, an attacker may repeatedly obtain partial information on behavior and finally derive relationships between pairs of users through the modeling of this statistical information. This work is an enhancement of a previously presented statistical disclosure attack. The improvement of the attack is based on the use of the EM algorithm to improve the estimation of messages sent by users and to derive what pairs of users really communicate. Two methods are presented using the EM algorithm and the best method is used over real email data over 32 different network domains. Results are encouraging with high classification and positive predictive value rates.

Privacy-Aware Collaborative Spam Filtering

While the concept of collaboration provides a natural defense against massive spam e-mails directed at large numbers of recipients, designing effective collaborative anti-spam systems raises several important research challenges. First and foremost, since e-mails may contain confidential information, any collaborative anti-spam approach has to guarantee strong privacy protection to the participating entities. Second, the continuously evolving nature of spam demands the collaborative techniques to be resilient to various kinds of camouflage attacks. Third, the collaboration has to be lightweight, efficient, and scalable. Toward addressing these challenges, this paper presents ALPACAS-a privacy-aware framework for collaborative spam filtering. In designing the ALPACAS framework, we make two unique contributions. The first is a feature-preserving message transformation technique that is highly resilient against the latest kinds of spam attacks. The second is a privacy-preserving protocol that provides enhanced privacy guarantees to the participating entities. Our experimental results conducted on a real e-mail data set shows that the proposed framework provides a 10 fold improvement in the false negative rate over the Bayesian-based Bogofilter when faced with one of the recent kinds of spam attacks. Further, the privacy breaches are extremely rare. This demonstrates the strong privacy protection provided by the ALPACAS system.

Mixtures of Gamma Distributions With Applications

This article proposes a Bayesian density estimation method based upon mixtures of gamma distributions. It considers both the cases of known mixture size, using a Gibbs sampling scheme with a Metropolis step, and unknown mixture size, using a reversible jump technique that allows us to move from one mixture size to another. We illustrate our methods using a number of simulated datasets, generated from distributions covering a wide range of cases: single distributions, mixtures of distributions with equal means and different variances, mixtures of distributions with different means and small variances and, finally, a distribution contaminated by low-weighted distributions with different means and equal, small variances. An application to estimation of some quantities for a M/G/1 queue is given, using real E-mail data from CNR-IAMI.