Benchmarking PyCaret AutoML Against BiLSTM for Fine-Grained Emotion Classification: A Comparative Study on 20-Class Emotion Detection

TL;DR

This study compares classical machine learning models and deep learning approaches, especially BiLSTM, for 20-class emotion classification, highlighting BiLSTM's superior performance in capturing contextual cues.

Contribution

It provides a comprehensive benchmark of ML and deep learning models on a large emotion dataset, emphasizing BiLSTM's effectiveness.

Findings

BiLSTM achieves 89% accuracy, outperforming SVM.

Traditional ML models like SVM are competitive and efficient.

Deep learning models better capture contextual emotional cues.

Abstract

Fine-grained emotion classification, which identifies specific emotional states such as happiness, anger, sadness, and fear, remains a challenging task in natural language processing. This study benchmarks classical machine learning and deep learning approaches for 20-class emotion classification using the 20-Emotion Text Classification Dataset containing 79,595 English sentences. On the machine learning side, Logistic Regression, Multinomial Naive Bayes, and Support Vector Machine are evaluated using TF-IDF features. On the deep learning side, Bidirectional Long Short-Term Memory, Gated Recurrent Unit, and a lightweight Transformer implemented in PyTorch are compared. The results show that BiLSTM achieves the best overall performance with 89% accuracy and a weighted F1-score of 0.89, slightly outperforming the best machine learning model, SVM, which reaches 88.11% accuracy. The findings indicate that while traditional machine learning models remain competitive and computationally efficient, sequence-based deep learning models better capture contextual emotional cues in text.