Source of Publication
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.
Institute of Electrical and Electronics Engineers (IEEE)
Sentiment analysis, Emotion recognition, Machine learning, Semantics, Computational modeling, Machine learning algorithms, Classification algorithms, Quantum computing
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Singh, Jaiteg; Ali, Farman; Shah, Babar; Bhangu, Kamalpreet Singh; and Kwak, Daehan, "Emotion Quantification Using Variational Quantum State Fidelity Estimation" (2022). All Works. 5443.
Indexed in Scopus
Open Access Type
Gold: This publication is openly available in an open access journal/series