Universal Spam Detection using Transfer Learning of BERT Model

Abstract

Several machine learning and deep learning algorithms were limited to one dataset of spam emails/texts, which waste valuable resources due to individual models. This research applied efficient classification of ham or spam emails in real-time scenarios. Deep learning transformer models become important by training on text data based on self-attention mechanisms. This manuscript demonstrated a novel universal spam detection model using pre-trained Google's Bidirectional Encoder Representations from Transformers (BERT) base uncased models with multiple spam datasets. Different methods for Enron, Spamassain, Lingspam, and Spamtext message classification datasets, were used to train models individually. The combined model is finetuned with hyperparameters of each model. When each model using its corresponding datasets, an F1-score is at 0.9 in the model architecture. The "universal model" was trained with four datasets and leveraged hyperparameters from each model. An overall accuracy reached 97%, with an F1 score at 0.96 combined across all four datasets.