A review of emotion sensing: categorization models and algorithms

• Published in: Multimedia tools and applications Vol. 79; no. 47-48; pp. 35553 - 35582
• Main Authors: Wang, Zhaoxia, Ho, Seng-Beng, Cambria, Erik
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2020; Springer Nature B.V
• Subjects: Algorithms; Attitudes; Classification; Computer Communication Networks; Computer Science; Data mining; Data Structures and Information Theory; Emotions; Multimedia Information Systems; Sentiment analysis; Special Purpose and Application-Based Systems; Affective computing; Sentiment analysis; Emotion categorization model; Emotion definition
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-019-08328-z

Sentiment analysis consists in the identification of the sentiment polarity associated with a target object, such as a book, a movie or a phone. Sentiments reflect feelings and attitudes, while emotions provide a finer characterization of the sentiments involved. With the huge number of comments generated daily on the Internet, besides sentiment analysis, emotion identification has drawn keen interest from different researchers, businessmen and politicians for polling public opinions and attitudes. This paper reviews and discusses existing emotion categorization models for emotion analysis and proposes methods that enhance existing emotion research. We carried out emotion analysis by inviting experts from different research areas to produce comprehensive results. Moreover, a computational emotion sensing model is proposed, and future improvements are discussed in this paper.