TY - JOUR
T1 - Emotion Quantification Using Variational Quantum State Fidelity Estimation
AU - Singh, Jaiteg
AU - Ali, Farman
AU - Shah, Babar
AU - Bhangu, Kamalpreet Singh
AU - Kwak, Daehan
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2022
Y1 - 2022
N2 - Sentiment analysis has been instrumental in developing artificial intelligence when applied to various domains. However, most sentiments and emotions are temporal and often exist in a complex manner. Several emotions can be experienced at the same time. Instead of recognizing only categorical information about emotions, there is a need to understand and quantify the intensity of emotions. The proposed research intends to investigate a quantum-inspired approach for quantifying emotional intensities in runtime. The inspiration comes from manifesting human cognition and decision-making capabilities, which may adopt a brief explanation through quantum theory. Quantum state fidelity was used to characterize states and estimate emotion intensities rendered by subjects from the Amsterdam Dynamic Facial Expression Set (ADFES) dataset. The Quantum variational classifier technique was used to perform this experiment on the IBM Quantum Experience platform. The proposed method successfully quantifies the intensities of joy, sadness, contempt, anger, surprise, and fear emotions of labelled subjects from the ADFES dataset.
AB - Sentiment analysis has been instrumental in developing artificial intelligence when applied to various domains. However, most sentiments and emotions are temporal and often exist in a complex manner. Several emotions can be experienced at the same time. Instead of recognizing only categorical information about emotions, there is a need to understand and quantify the intensity of emotions. The proposed research intends to investigate a quantum-inspired approach for quantifying emotional intensities in runtime. The inspiration comes from manifesting human cognition and decision-making capabilities, which may adopt a brief explanation through quantum theory. Quantum state fidelity was used to characterize states and estimate emotion intensities rendered by subjects from the Amsterdam Dynamic Facial Expression Set (ADFES) dataset. The Quantum variational classifier technique was used to perform this experiment on the IBM Quantum Experience platform. The proposed method successfully quantifies the intensities of joy, sadness, contempt, anger, surprise, and fear emotions of labelled subjects from the ADFES dataset.
KW - Emotion detection
KW - quantification of emotions
KW - quantum computation
KW - quantum machine learning
KW - sentiment analysis
UR - http://www.scopus.com/inward/record.url?scp=85141489106&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2022.3216890
DO - 10.1109/ACCESS.2022.3216890
M3 - Article
AN - SCOPUS:85141489106
SN - 2169-3536
VL - 10
SP - 115108
EP - 115119
JO - IEEE Access
JF - IEEE Access
ER -