Open AccessText‐based emotion detection: Advances, challenges, and opportunities
Author(s) -
Acheampong Francisca Adoma,
Wenyu Chen,
NunooMensah Henry
Publication year - 2020
Publication title -
engineering reports
Language(s) - English
Resource type - Journals
ISSN - 2577-8196
DOI - 10.1002/eng2.12189
Subject(s) - sentiment analysis , field (mathematics) , data science , computer science , deliverable , strengths and weaknesses , relation (database) , service provider , service (business) , emotion detection , emotion recognition , data mining , artificial intelligence , psychology , engineering , marketing , systems engineering , social psychology , mathematics , pure mathematics , business
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.