The DOI matches its Crossref record; 73 of the 73 references it cites were found in a registry or an index.
Text‐based emotion detection: Advances, challenges, and opportunities
Open accessAbstract
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.
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BackgroundMethodInfluentialBag of Tricks for Efficient Text Classification
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Evidence for a three-factor theory of emotions
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Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
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MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
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Affective Computing and Sentiment Analysis
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Cited by
A survey on sentiment analysis methods, applications, and challenges
Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10144-1 · Cited by 1,528
Transformer models for text-based emotion detection: a review of BERT-based approaches
Acheampong, F. A., Nunoo-Mensah, H., & Chen, W. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-09958-2 · Cited by 659
Self-citationConversational Agents: Goals, Technologies, Vision and Challenges
Allouch, M., Azaria, A., & Azoulay, R. (2021) · Sensors · doi:10.3390/s21248448 · Cited by 168
RePrompt: Automatic Prompt Editing to Refine AI-Generative Art Towards Precise Expressions
Wang, Y., Shen, S., & Lim, B. Y. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581402 · Cited by 120
Metadata
- Authors
- Acheampong, F. A., Wenyu, C., & Nunoo‐Mensah, H.
- Venue
- Engineering Reports
- Publisher
- Wiley
- Published
- 2020-05-28
- Volume
- 2
- Issue
- 7
- ISSN
- 2577-8196 (ISSN-L)
- OpenAlex
- W3032735579
- Cited by
- 367
- References
- 73
- FWCI
- 19.04
- Type
- journal article
- Language
- en
- Open access
- gold
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