Text‐based emotion detection: Advances, challenges, and opportunities
By Francisca Adoma Acheampong, Chen Wenyu, Henry Nunoo-Mensah. Engineering Reports, 2020
349 citations recorded by the source corpus.
Emotion detection (ED) is a branch of sentiment analysis that deals with the extraction and analysis of emotions. The evolution of Web 2.0 has put text mining and analysis at the frontiers of organizational success. It helps service providers provide tailor‐made services to their customers. Numerous studies are being carried out in the area of text mining and analysis due to the ease in sourcing for data and the vast benefits its deliverable offers. This article surveys the concept of ED from texts and highlights the main approaches adopted by researchers in the design of text‐based ED systems. The article further discusses some recent state‐of‐the‐art proposals in the field. The proposals are discussed in relation to their major contributions, approaches employed, datasets used, results obtained, strengths, and their weaknesses. Also, emotion‐labeled data sources are presented to provide neophytes with eligible text datasets for ED. Finally, the article presents some open issues and future research direction for text‐based ED.
Subfields: Artificial Intelligence, Social Psychology
View Paper: https://www.semanticscholar.org/paper/855459d82f3e72b0631c29cff731964a95d38712
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Citations in the corpus
- Transformer models for text-based emotion detection: a review of BERT-based approaches (2021, background): “The approaches for recognizing emotions from texts as outlined by Acheampong et al. (2020) inferred that Machine learning approaches are currently achieving the state of the art with deep learning techniques in the lead.”