Words Speak Louder Than Actions: Decoding Emotions Through NLP - Paksoy, Melda; Bakal, Gokhan (Institute of Electrical and Electronics Engineers Inc., 2024)
Emotion detection in text remains a significant challenge in Natural Language Processing due to human emotions' complexity and subtle nuances. This paper presents multiple experimental models for emotion classification using an up-to-date dataset curated to address 13 emotions implied in Twitter posts. We evaluated various machine learning (ML) models, including Logistic Regression, Random Forest, SVM, and XGBoost, alongside deep learning (DL) architectures such as LSTM and CNN. Our results demonstrate the efficacy of deep learning models, particularly the CNN model by achieving an impressive F1 score of 0.99. This study contributes to emotion detection capabilities, paving the way for more nuanced and accurate sentiment analysis (SA) in various text analysis applications. © 2025 Elsevier B.V., All rights reserved.
Keywords: Deep Learning; Emotion Detection; Machine Learning; Text Mining; Contrastive Learning; Deep Learning; Emotion Recognition; Federated Learning; Emotion Classification; Emotion Detection; Experimental Modelling; Human Emotion; Language Processing; Machine-Learning; Natural Languages; Text-Mining; Twitter Posts; Adversarial Machine Learning