eprintid: 18331 rev_number: 2 eprint_status: archive userid: 1 dir: disk0/00/01/83/31 datestamp: 2024-06-04 14:10:31 lastmod: 2024-06-04 14:10:31 status_changed: 2024-06-04 14:02:39 type: article metadata_visibility: show creators_name: Ahanin, Z. creators_name: Ismail, M.A. creators_name: Singh, N.S.S. creators_name: AL-Ashmori, A. title: Hybrid Feature Extraction for Multi-Label Emotion Classification in English Text Messages ispublished: pub keywords: artificial neural network; decision making; knowledge; learning; training note: cited By 6 abstract: Emotions are vital for identifying an individual�s attitude and mental condition. Detecting and classifying emotions in Natural Language Processing applications can improve Human�Computer Interaction systems, leading to effective decision making in organizations. Several studies on emotion classification have employed word embedding as a feature extraction method, but they do not consider the sentiment polarity of words. Moreover, relying exclusively on deep learning models to extract linguistic features may result in misclassifications due to the small training dataset. In this paper, we present a hybrid feature extraction model using human-engineered features combined with deep learning based features for emotion classification in English text. The proposed model uses data augmentation, captures contextual information, integrates knowledge from lexical resources, and employs deep learning models, including Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Encoder Representation and Transformer (BERT), to address the issues mentioned above. The proposed model with hybrid features attained the highest Jaccard accuracy on two of the benchmark datasets, with 68.40 on SemEval-2018 and 53.45 on the GoEmotions dataset. The results show the significance of the proposed technique, and we can conclude that the incorporation of the hybrid features improves the performance of the baseline models. © 2023 by the authors. date: 2023 official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169150020&doi=10.3390%2fsu151612539&partnerID=40&md5=8985e6e6b20628d2366925d67d0bf345 id_number: 10.3390/su151612539 full_text_status: none publication: Sustainability (Switzerland) volume: 15 number: 16 refereed: TRUE citation: Ahanin, Z. and Ismail, M.A. and Singh, N.S.S. and AL-Ashmori, A. (2023) Hybrid Feature Extraction for Multi-Label Emotion Classification in English Text Messages. Sustainability (Switzerland), 15 (16).