Sentiment analysis plays a crucial role in domains such as financial news, user reviews, and public opinion monitoring, yet existing approaches face challenges when dealing with long and domain-specific texts due to semantic dilution, insufficient context modeling, and dispersed emotional signals. To address these issues, a multi-granularity attention-based sentiment analysis model built on a transformer backbone is proposed. The framework integrates sentence-level and document-level hierarchical modeling, a different-dimensional embedding strategy, and a cross-granularity contrastive fusion mechanism, thereby achieving unified representation and dynamic alignment of local and global emotional features. Static word embeddings combined with dynamic contextual embeddings enhance both semantic stability and context sensitivity, while the cross-granularity fusion module alleviates sparsity and dispersion of emotional cues in long texts, improving robustness and discriminability. Extensive experiments on multiple benchmark datasets demonstrate the effectiveness of the proposed model. On the Financial Forum Reviews dataset, it achieves an accuracy of 0.932, precision of 0.928, recall of 0.925, F1-score of 0.926, and AUC of 0.951, surpassing state-of-the-art baselines such as BERT and RoBERTa. On the Financial Product User Reviews dataset, the model obtains an accuracy of 0.902, precision of 0.898, recall of 0.894, and AUC of 0.921, showing significant improvements for short-text sentiment tasks. On the Financial News dataset, it achieves an accuracy of 0.874, precision of 0.869, recall of 0.864, and AUC of 0.895, highlighting its strong adaptability to professional and domain-specific texts. Ablation studies further confirm that the multi-granularity transformer structure, the different-dimensional embedding strategy, and the cross-granularity fusion module each contribute critically to overall performance improvements.

Anomalous transaction behaviors in cryptocurrency markets exhibit high concealment, substantial diversity, and strong cross-modal coupling, making traditional rule-based or single-feature analytical methods insufficient for reliable detection in real-world environments. To address the research focus, a data-centric multimodal anomaly detection framework integrating generative augmentation, latent distribution modeling, and dual-branch real-time detection is proposed. The method employs a generative adversarial network with feature-consistency constraints to mitigate the scarcity of fraudulent samples, and adopts a multi-domain variational modeling strategy to learn the latent distribution of normal behaviors, enabling stable anomaly scoring. By combining the long-range temporal modeling capability of Transformer architectures with the sensitivity of online clustering to local structural deviations, the system dynamically integrates global and local information through an adaptive risk fusion mechanism, thereby enhancing robustness and real-time detection capability. Experimental results demonstrate that the generative augmentation module yields substantial improvements, increasing the recall from $0.421$ to $0.671$ and the F1-score to $0.692$. In anomaly distribution modeling, the multi-domain VAE achieves an area under the curve (AUC) of $0.854$ and an F1-score of $0.660$, significantly outperforming traditional One-Class SVM and autoencoder baselines. Multimodal fusion experiments further verify the complementarity of the dual-branch detection structure, with the adaptive fusion model achieving an AUC of $0.884$, an F1-score of $0.713$, and reducing the false positive rate to $0.087$. Ablation studies show that the complete model surpasses any individual module in terms of precision, recall, and F1-score, confirming the synergistic benefits of its integrated components. Overall, the proposed framework achieves high accuracy and high recall in data-scarce, structurally complex, and latency-sensitive cryptocurrency scenarios, providing a scalable and efficient solution for deploying data-centric artificial intelligence in financial security applications.

Smart greenhouse management requires decisions that balance yields, costs, resource use, emissions, and revenue. Existing threshold-based controls and many learning models use sensor data mainly for environmental feedback or yield prediction, leaving limited support for operational decisions tied to farm profitability and resource efficiency. This study proposes AER-SFRDNet, a multisource sensor fusion and deep reinforcement learning framework for revenue-aware greenhouse regulation. Crop images, environmental sensor sequences, management records, resource consumption data, and economic variables from facility tomato production are integrated into a unified agro-economic state representation. A yield–cost–revenue prediction module estimates the yield, resource consumption, production cost, sales revenue, and net revenue. These outputs guide a reinforcement learning module that optimizes continuous actions including irrigation, ventilation, supplemental lighting, heating, and CO₂ application. A comparison with traditional machine learning, unimodal deep learning, multimodal learning, and reinforcement learning baselines shows lower prediction errors and improved regulation outcomes. AER-SFRDNet achieves a yield MAE of $0.486$, yield RMSE of $0.674$, yield $R^2$ of $0.893$, cost RMSE of $0.613$, and net revenue MAPE of $10.82$. In regulation experiments, the cumulative net revenue and input–output ratio reach $1.276$ and $1.263$, while the normalized energy use, water use, and carbon emissions per unit yield decrease to $0.752$, $0.781$, and $0.741$, respectively. The results suggest that agro-economic state modeling can support greenhouse decisions that consider both revenue and sustainability-related resource efficiency.