From Logistic Regression to GPT-2: Building a Complete Spam Detection & Sentiment Analysis Pipeline

The article presents a comprehensive spam detection and sentiment analysis pipeline using eight different models on an imbalanced dataset. It highlights that F1-score and precision-recall curves are more informative than accuracy for evaluating performance in such scenarios. BERT and BiLSTM emerge as top performers, while GPT-2 shows potential with proper threshold tuning. The study concludes by enriching the dataset with sentiment analysis to identify patterns linking spam messages to positive sentiments.

Read the full article at Towards AI - Medium

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