A REVIEW ON MACHINE LEARNING AND DEEP LEARNING TECHNIQUES FOR TEXTUAL EMOTION ANALYSIS ON SOCIAL NETWORKS
DOI:
https://doi.org/10.46754/jmsi.2024.10.008Keywords:
Emotion detection, Natural language processing, Machine learning, Deep learningAbstract
In recent times emotion detection has achieved significant attention in the field of Natural Language Processing (NLP) due to the abundance of text data available on different social network platforms like Twitter, LinkedIn and Reddit. This paper presents a thorough review of existing emotion detection techniques on text analysis. The methodology involves a comparative analysis of different machine
learning and deep learning models, approaches and datasets utilised for emotion detection. The article discusses the limitations and challenges of traditional methods and delves into the theoretical foundations of machine learning and deep learning techniques such as SVM, CNN and BERT. By exploring the current state-of-the-art in emotion detection from social networks, this review aims to provide insight for researchers to advance the field of emotion detection.
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