Positive Emotion Recognition—A Survey of Computational Models
Year: 2025
Type: article
Study design: Systematic Review
Abstract: In this paper, we provide a systematic survey of works on positive emotion recognition and detection. We queried major research paper databases for 12 different emotional states: admiration, amusement, awe, compassion, contentment, elation, enthusiasm, excitement, gratitude, pride, relief, and sympathy. From an initial pool of more than 800 papers, we selected 81 that propose recognition models, and categorized them according to the modality and data type used for recognition. According to the results, the most frequently occurring labels are amusement and excitement, followed by compassion and contentment. In terms of data type, the highest number of solutions utilize various physiological signals, with visual data being the second most common. As the first survey to focus on a large number of distinct positive emotions, our paper contributes to Affective Computing and, more broadly, to Human-Computer Interaction by demonstrating that: 1) the topic of positive emotion recognition remains largely unexplored, despite numerous potential applications; 2) there are significant shortcomings in the research on positive emotion recognition, including the lack of relevant datasets, contextual information and adequate data collection procedures. Finally, we provide suggestions and guidelines to support future research in this area.
Source: IEEE Access
Authors: Irem Arici, Radoslaw Niewiadomski
Institution: University of Genoa
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W4411948803