Efficient Email Classification Algorithm for Better Customer Support

Deepika, M.; Hegde, Nagaratna P.

doi:10.1007/978-981-16-9705-0_22

Cited by 1 publication

(1 citation statement)

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“…Hu et al [12], Deepika and Hegde [13] classified emails based on their titles into four different categories: Sexual, financial, and marketing applications. They mainly focus on the features extracted only from the email header message.…”

Section: Related Workmentioning

confidence: 99%

Feature Extraction and Classification of Email Spam Detection Using IMTF-IDF+Skip-Thought Vectors

Mallampati¹,

Hedge²

2022

ISI

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Spam is a major concern in present emails, and there are several reasons for sending spam emails. The two most common ones are advertising and fraud. If supported by suitable preprocessing approaches, the detection algorithm for spam email or spam classifier will function effectively (removal of noise, removal of stop words, stemming, lemmatization, term frequency). Spam that combines both text and image components is referred to as hybrid spam. Compared to spam emails with images and text, it is more unsafe and complex. To distinguish spam or ham, we must use an effective and smart approach in order to have a strong representation of emails and improve classification performance. In this paper, we propose a multi-modal architecture relying on a feature model (MMA-FM) that concatenates two embedding vectors. The text and image sections of the similar emails were separated using a hybrid model (IMTF-IDF+Skip-thoughts) and the convolutional neural network (CNN) as a feature extraction technique. The extracted features are concatenated and given to Naïve Bayes (NB) and Support Vector Machine (SVM) models to classify hybrid email as either spam or ham. In this paper we used two hybrid datasets: Enron, Dredze, and TREC 2007, which are publicly accessible corpora. Our results show that the SVM model provides an accuracy of 99.16%, which is higher when compared to the Naïve Bayes method.

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Section: Related Workmentioning

confidence: 99%