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Hybrid Feature Extraction for Multi-Label Emotion Classification in English Text Messages by Zahra Ahanin; Maizatul Akmar Ismail; Narinderjit Singh Sawaran Singh; Ammar AL-Ashmori is a Environmental Science article available to read on EtoBox.

What is Hybrid Feature Extraction for Multi-Label Emotion Classification in English Text Messages about?

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

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Author

Zahra Ahanin; Maizatul Akmar Ismail; Narinderjit Singh Sawaran Singh; Ammar AL-Ashmori

Publisher

MDPI AG

Published

2023

Language

EN

Field

Environmental Science (Social Sciences)