Twitter Sentiment Analysis via Chaotic Quantum Fruit Fly Optimization: Enhancing Feature Selection and Classification
Bibliographic record
Abstract
This research presents a novel feature selection framework-Chaotic Quantum Fruit Fly Optimization Algorithm (CQFOA)-designed to enhance Twitter sentiment analysis.CQFOA extends the standard Fruit Fly Optimization Algorithm (FOA) by incorporating two advanced mechanisms: (1) a chaotic mapping strategy to maintain population diversity and prevent premature convergence; and (2) quantum-behaved position updating using probabilistic rotation gates for global exploration.The integration of these strategies improves the algorithm's ability to handle the high dimensionality and the noise characteristic of Twitter data.CQFOA was applied as a feature selector prior to classification by Convolutional Neural Networks (CNN); Recurrent Neural Networks (RNN) and Recursive Neural Networks.Compared to traditional feature selection techniques-Particle Swarm Optimization (PSO); Genetic Algorithm (GA); Artificial Bee Colony (ABC); and the conventional FOA-CQFOA achieved higher performance across multiple evaluation metrics.In particular, average improvements were observed in accuracy (up to 94.13%); precision (96.84%); recall (97.24%); and F-measure (98.97%).These results were validated using 10-fold crossvalidation and assessed via paired t-tests, confirming statistically significant improvements (p < 0.05) over baseline methods.To ensure reliability, class distribution and data preprocessing strategies were rigorously monitored to mitigate overfitting and class imbalance.The proposed CQFOA framework demonstrates robustness in highdimensional noisy data environments and offers a reproducible pipeline for sentiment classification tasks in social media analytics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".