Paksoy, MeldaBakal, Gokhan2025-09-252025-09-2520249798350365887https://doi.org/10.1109/UBMK63289.2024.10773493https://hdl.handle.net/20.500.12573/4976Emotion 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.eninfo:eu-repo/semantics/closedAccessDeep LearningEmotion DetectionMachine LearningText MiningContrastive LearningDeep LearningEmotion RecognitionFederated LearningEmotion ClassificationEmotion DetectionExperimental ModellingHuman EmotionLanguage ProcessingMachine-LearningNatural LanguagesText-MiningTwitter PostsAdversarial Machine LearningConference Object10.1109/UBMK63289.2024.107734932-s2.0-85215522321