A Chinese positive emotion database was established using 22 film clips, achieving a baseline classification accuracy of 44.66% for four major emotion categories using physiological signals.
Observational (n=312)
The establishment of the Chinese Positive Emotion Database provides a standardized set of video clips and physiological signals for emotion elicitation and analysis.
Positive emotions are of great significance to people's daily life, such as human-computer/robot interaction. However, the structure of extensive positive emotions is not clear yet and effective standardized inducing materials containing as many positive emotional categories as possible are lacking. Thus, this article aims to establish a Chinese positive emotion database (CPED) to (1) effectively elicit positive emotion categories as many as possible, (2) provide both the subjective feelings of different positive emotions and a corresponding peripheral physiological database, and (3) explore the structure and framework of positive emotion categories. 42 video clips of 16 positive emotion categories were screened from 1000+ online clips. Then a total of 312 participants watched and rated these video clips during which GSR and PPG signals were recorded. 34 video clips that met hit rate and intensity standards were systemically clustered into four emotion categories (empathy, fun, creativity and esteem). Eventually, 22 film clips of these four major categories formed the CPED database. A total of 84 features from GSR and PPG signals were extracted and entered into RF, SVM, DBN and LSTM classifiers that serves as baseline classification methods. A classification accuracy of 44.66 percent for four major categories of positive emotions was achieved.
Zhang et al. (Fri,) conducted a observational in Positive emotion elicitation (n=312). Video clips for positive emotion elicitation was evaluated on Classification accuracy for four major categories of positive emotions. A Chinese positive emotion database was established using 22 film clips, achieving a baseline classification accuracy of 44.66% for four major emotion categories using physiological signals.