Comparative Analysis of Deep Learning Architectures for Emotion Recognition in Text - Gregorius Airlangga (LPPM STIKOM Tunas Bangsa, 2024)
This study delves into the intricacies of emotion recognition within textual data, presenting a comprehensive analysis of three prominent deep learning models: Long Short-Term Memory networks (LSTMs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). Employing a 5-fold cross-validation methodology, the research meticulously evaluates each model's performance in accurately classifying a spectrum of emotions, using metrics such as accuracy, precision, recall, and F1 score. Results indicate that LSTMs outperform their counterparts with an accuracy of 93.48%, closely followed by CNNs at 91.78%, while RNNs lag, showcasing the importance of sophisticated architectural features in handling complex emotional nuances. The study not only highlights the strengths and limitations of each model but also sheds light on the significant role of temporal and contextual understanding in emotion recognition tasks. Through this investigation, we provide insights into the evolving landscape of natural language processing and its capability to decode human emotions, proposing directions for future research in enhancing model performance. This work has broader implications for applications in mental health, customer service, and social media analysis, aiming to refine the interaction between humans and machines in understanding and processing emotional content