Adaptive Chaotic Quantum‐Inspired Fruit Fly Optimisation (A‐CQFOA) for Robust Feature Selection in Sentiment140, SST‐2, and Telecom Twitter Sentiment Classification
SatheeshKumar Palanisamy, Jeevitha Kandasamy, Sathishkumar Nallusamy, Sachin ShresthaABSTRACT
This letter proposes A‐CQFOA, an adaptive chaotic quantum‐inspired fruit fly optimisation framework for multi‐objective feature selection coupled with CNN, LSTM and Bi‐LSTM sentiment classifiers. It unifies a logistic chaotic map and quantum rotation operator within a single FOA‐based selector, with a classifier‐coupled fitness function optimising accuracy, feature compactness and prediction consistency, and an adaptive stagnation‐aware chaos‐decay mechanism. Over 30 independent 10‐fold CV runs on Sentiment140 (1.6 M tweets), SST‐2 (67k reviews) and Telecom Twitter (22k opinions), A‐CQFOA obtains mean accuracies of 0.9267, 0.9412 and 0.9328—gains of +3.17–3.28% over PSO while reducing features by 35%–45%. Open‐set AUC = 0.997 and >94% few‐shot accuracy (5 samples/class), all at p < 0.01.