Content Based Email Spam Classifier as a Web Application Using Naïve Bayes Classifier

Chakraborty, Arpita; Das, Utpol Kanti; Sikder, Juel; Maimuna, Maisha; Sarek, Kamrul Islam

doi:10.1007/978-3-031-19958-5_36

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…Deployed as a user-friendly web application, it allows input for real-time evaluation and classification, demonstrating the model's adaptability and practical application in spam email classification. In the developed web application the predefined NLTK library was used for pre-processing (5) . Moutafis et al (2023) propose using various machine learning classifiers to classify raw emails as spam or benign.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Spam Email Classification Using Effective Preprocessing Strategies and Optimal Machine Learning Algorithms

Ghogare,

Dawoodi,

Patil

2024

IJST

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Objective: This article proposes a content-based spam email classification by applying various text pre-processing techniques. NLP techniques have been applied to pre-process the content of an email to get the optimal performance of spam email classification using machine learning. Method: Several combinations of pre-processing methods, such as stopping, removing tags, converting to lower case, removing punctuation, removing special characters, and natural language processing, were applied to the extracted content from the email with machine learning algorithms like NB, SVM, and RF to classify an email as ham or spam. The standard datasets like Enron and SpamAssassin, along with the personal email dataset collected from Yahoo Mail, are used to evaluate the performance of the models. Findings: Applying stemming in pre-processing to the RF classifier yielded the best results, achieving 99.2% accuracy on the SpamAssassin dataset and 99.3% accuracy on the Enron dataset. Lemmatization followed closely with 99% accuracy. In real-world testing on a personal Yahoo email dataset, the proposed method significantly improved accuracy from 89.82% to 97.28% compared to the email service provider's built-in classifier. Additionally, the study found that SVM performs accurately when stop words are retained. Novelty: This article introduces a unique perspective by highlighting the fine-tuning of pre-processing techniques. The focus is on removing tags and certain special characters, while retaining those that improve spam email classification accuracy. Unlike prior works that primarily emphasize algorithmic approaches and pre-defined processing functions, our research delves into the intricacies of data preparation, showcasing its significant impact on spam email classifiers. These findings emphasize the crucial role of pre-processing and contribute to a more nuanced understanding of effective strategies for robust spam detection. Keywords: Spam, Classification, Pre-processing, NLP, Machine Learning

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Section: Introductionmentioning

confidence: 99%