Over the past decades, social media attracted individuals to express their feelings on any topic or item, resulting in an incremental growth in the size of created data. These feelings and unstructured data paved the path for business organizations to gather information and build statistical analysis. Various machine learning and natural language processing-based approaches are used for sentiment and emotion analysis. Moreover, deep learning-based approaches recently gained popularity due to their remarkable performance in text analysis. This paper provides a comprehensive overview of the prominent machine learning models applied in emotion analysis. It explores various emotion analysis taxonomies, in addition to the constraints of prevalent deep learning architectures. The paper also reviews some of the previously presented contributions in emotion analysis with a focus on deep learning methodologies as well as the most common datasets. It presents a comprehensive comparison between several emotion analysis models. This paper demonstrates the effectiveness of learning-based techniques in tackling emotion analysis challenges.
Zidan, Mahinda Mahmoud Samy; Elhenawy, Ibrahim; Abas, Ahmed R.; and Othman, Mahmoud
"TEXTUAL EMOTION DETECTION APPROACHES: A SURVEY,"
Future Computing and Informatics Journal: Vol. 7:
1, Article 3.
Available at: https://digitalcommons.aaru.edu.jo/fcij/vol7/iss1/3