@Article{jai.2026.074171,
AUTHOR = {Anitya Kumar Gupta, Pankaj Vaidya, Anurag Rana},
TITLE = {Mathematical Framework for Detecting Sentiments Analysis Using Quantum Computing},
JOURNAL = {Journal on Artificial Intelligence},
VOLUME = {8},
YEAR = {2026},
NUMBER = {1},
PAGES = {377--402},
URL = {http://www.techscience.com/jai/v8n1/68054},
ISSN = {2579-003X},
ABSTRACT = {Sentiment analysis aims at determining the stance or point of view of a topic, author, or speaker about a certain topic, document, or event. The given paper proposes a hybrid quantum-classical model of sentiment classification, which is known as Complex-Valued Quantum-Enhanced Recurrent Neural Network (CQRNN). The model combines Quantum Long Short-Term Memory (QLSTM) and Quantum Gated Recurrent Units (QGRU) with the use of Variational Quantum Circuits (VQCs) and complex-valued embeddings designed at the level of semantic information, both in the real and imaginary domains. Experiments on benchmark sentiment datasets show that CQRNN can be fine-tuned and reach an accuracy of 85% on noisy and ambiguous data, and this is a demonstration of its strength and better ability to discriminate between similar sentiment expressions like neutral and negative expressions. The performance of quantum-inspired architectures in sentiment classification compared with the traditional models, including SVM and Random Forest, establishes the potential. The article prepares the basis of benchmarking CQRNN to the transformer-based models (BERT, RoBERTa) and implementing it on actual quantum hardware.},
DOI = {10.32604/jai.2026.074171}
}