A Comparison of Traditional Machine Learning Algorithms and LSTM-Based Deep Learning Models for Email Sentiment Analysis

TL;DR

This study compares traditional machine learning algorithms and LSTM-based deep learning models for email sentiment analysis, highlighting SVM's superior efficiency and accuracy, while noting LSTM's high recall but higher computational cost.

Contribution

It provides a comprehensive performance comparison between traditional classifiers and LSTM models using Word2Vec embeddings for email sentiment detection.

Findings

SVM with linear kernel achieves 98.74% accuracy

LSTM demonstrates high recall but requires more computational time

Traditional classifiers are robust in dense vector spaces

Abstract

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.