Sentiment analysis aims to study, analyse and identify the sentiment polarity contained in subjective documents. In the realm of natural language processing (NLP), the study of sentiment analysis and its subtask research is a hot topic, which has very important significance. The existing sentiment analysis methods based on sentiment lexicon and machine learning take into account contextual semantic information, but these methods still lack the ability to utilize context information, so they cannot effectively encode context information. Inspired by the concept of density matrix in quantum mechanics, we propose a sentiment analysis method, named Complex-valued Quantum-enhanced Long Short-term Memory Neural Network (CQLSTM). It leverages complex-valued embedding to incorporate more semantic information and utilizes the Complex-valued Quantum-enhanced Long Short-term Memory Neural Network for feature extraction. Specifically, a complex-valued neural network based on density matrix is used to capture interactions between words (i.e., the correlation between words). Additionally, the Complex-valued Quantum-enhanced Long Short-term Memory Neural Network, which is inspired by the quantum measurement theory and quantum long short-term memory neural network, is developed to learn interactions between sentences (i.e., contextual semantic information). This approach effectively encodes semantic dependencies, which reflects the dispersion of words in the embedded space of sentences and comprehensively captures interactive information and long-term dependencies among the emotional features between words. Comparative experiments were performed on four sentiment analysis datasets using five traditional models, showcasing the effectiveness of the CQLSTM model.

To address the issues of missed and false detections caused by small object sizes, dense object distribution, and complex scenes in drone aerial images, this study proposes a drone-view object-detection algorithm based on YOLOv7-tiny with a Partial_C_Detect detection head. The algorithm’s performance in handling object occlusion and multi-scale detection is enhanced by introducing the VarifocalLoss loss function and improving the feature fusion network to BiFPN. Furthermore, incorporating the novel Partial_C_Detect detection head and Adaptive Kernel Convolution (AKConv) improves the detection capabilities for small and dynamically changing objects. In addition, introducing the Dilated Weighted Residual (DWR) attention module optimizes the information processing flow, enhancing the algorithm’s ability to capture key information, especially in complex backgrounds. These enhancements collectively enable the model to balance high detection accuracy and computational efficiency, making it well-suited for resource-constrained UAV platforms. Experiments conducted on the VisDrone2019 dataset show that the improved algorithm achieves a mAP@0.5 of 38.2%, with a model size of 29.01 MB and a computational complexity of 16.2 G. Compared to the original YOLOv7-tiny algorithm, the mAP@0.5 improves by 2.9%, and the algorithm performs better in other key performance metrics, demonstrating its adaptability and robustness in drone aerial image object-detection tasks.