A Comparison of Traditional Machine Learning Algorithms and LSTM-Based Deep Learning Models for Email Sentiment Analysis
Provides benchmark comparison for email filtering systems, but incremental as it applies known methods to a standard task.
This study compares traditional ML models (SVM, Logistic Regression, Naive Bayes) with LSTM for email sentiment analysis, finding that SVM with linear kernel achieves 98.74% accuracy, outperforming LSTM in efficiency while LSTM shows better recall for spam sentiments.
The rapid growth of electronic communication has necessitated more robust systems for email classification and sentiment detection. This study presents a comparative performance analysis between traditional machine learning algorithms and deep learning architectures, specifically focusing on Support Vector Machines (SVMs), Logistic Regression, Naive Bayes, and Long Short-Term Memory (LSTM). Utilizing Word2Vec embeddings for feature representation, our experimental results indicate that the SVM model with a linear kernel achieves the highest efficiency and accuracy, reaching a peak performance of 98.74%. While the LSTM model demonstrates exceptional recall capabilities in detecting spam-related sentiments, it requires significantly more computational time compared to discriminative statistical models. Detailed evaluations via confusion matrices further reveal that traditional classifiers remain highly robust for dense vector spaces. This research concludes that for email detection tasks, SVM offers the most optimal balance between predictive precision and processing speed. These findings provide critical insights for developing high-performance automated email filtering systems in professional and academic environments.