Spam Detection and Classification Based on DistilBERT Deep Learning Algorithm
DOI:
https://doi.org/10.5281/zenodo.11180575Keywords:
spam detection, distilbert, accuracy, classificationAbstract
This paper discusses the importance of spam classification in the field of information security. With the popularity of the Internet and email, spam has become one of the major issues affecting user experience and information security. The study begins with preprocessing text data in various ways, including converting to lowercase, removing irrelevant content, links, punctuation, etc., and filtering deactivated words and words of length 1. By applying the DistilBERT model to the text classification task, the results show that it achieves 93% accuracy in spam classification, effectively distinguishing between spam and non-spam emails. The confusion matrix showed that 18,500 emails were correctly classified and a small number of spam emails were misclassified as non-spam emails. Overall, the DistilBERT model showed high accuracy in spam classification, but more algorithms are still expected to emerge to improve the prediction accuracy. This study provides a useful reference for improving spam filtering systems in the future, which is expected to further enhance user experience and information security.
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Copyright (c) 2024 Tianrui Liu, Shaojie Li, Yushan Dong, Yuhong Mo, Shuyao He
This work is licensed under a Creative Commons Attribution 4.0 International License.
