Today, people on the web share their feelings and emotions with the help of various communication tools, one of the most common of which is the expression of feelings in textual content such as social media posts, online store reviews, and user reviews. Emotion detection in text is a branch of sentiment analysis that aims to identify different types of human emotion in the text. This scientific field helps manufacturers and service providers to be aware of their weaknesses and strengths, and to provide better services to customers. In recent years, emotion recognition in text has become an attractive research field due to its wide applications in business, economics, politics, medicine, psychology, and sociology. In this article, the problem of emotion classification in text and its solution methods will be investigated with emphasis on deep learning. Also, a brief description of the latest deep learning solutions that have been used in recent years to classify emotion in text will be discussed. In addition, a number of labeled datasets, the most important open issues in emotion recognition, and future research directions will also be presented, which can be a good guide for new researchers in this field.
Rasouli, M., & Kiani, V. (2023). A survey on deep learning methods for text-based emotion classification: Advances, challenges, and opportunities. Soft Computing Journal, (), -. doi: 10.22052/scj.2023.248812.1126
MLA
Mahdi Rasouli; Vahid Kiani. "A survey on deep learning methods for text-based emotion classification: Advances, challenges, and opportunities". Soft Computing Journal, , , 2023, -. doi: 10.22052/scj.2023.248812.1126
HARVARD
Rasouli, M., Kiani, V. (2023). 'A survey on deep learning methods for text-based emotion classification: Advances, challenges, and opportunities', Soft Computing Journal, (), pp. -. doi: 10.22052/scj.2023.248812.1126
VANCOUVER
Rasouli, M., Kiani, V. A survey on deep learning methods for text-based emotion classification: Advances, challenges, and opportunities. Soft Computing Journal, 2023; (): -. doi: 10.22052/scj.2023.248812.1126
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