Metrics F1-score (Benchmark techniques VS MLEn).

In the dynamic domain of logistics, effective communication is essential for streamlined operations. Our innovative solution, the Multi-Labeling Ensemble (MLEn), tackles the intricate task of extracting multi-labeled data, employing advanced techniques for accurate preprocessing of textual data through the NLTK toolkit. This approach is carefully tailored to the prevailing language used in logistics communication. MLEn utilizes innovative methods, including sentiment intensity analysis, Word2Vec, and Doc2Vec, ensuring comprehensive feature extraction. This proves particularly suitable for logistics in e-commerce, capturing nuanced communication essential for efficient operations. Ethical considerations are a cornerstone in logistics communication, and MLEn plays a pivotal role in detecting and categorizing inappropriate language, aligning inherently with ethical norms. Leveraging Tf-IDF and Vader for feature enhancement, MLEn adeptly discerns and labels ethically sensitive content in logistics communication. Across diverse datasets, including Emotions, MLEn consistently achieves impressive accuracy levels ranging from 92% to 97%, establishing its superiority in the logistics context. Particularly, our proposed method, DenseNet-EHO, outperforms BERT by 8% and surpasses other techniques by a 15-25% efficiency. A comprehensive analysis, considering metrics such as precision, recall, F1-score, Ranking Loss, Jaccard Similarity, AUC-ROC, sensitivity, and time complexity, underscores DenseNet-EHO’s efficiency, aligning with the practical demands within the logistics track. Our research significantly contributes to enhancing precision, diversity, and computational efficiency in aspect-based sentiment analysis within logistics. By integrating cutting-edge preprocessing, sentiment intensity analysis, and vectorization, MLEn emerges as a robust framework for multi-label datasets, consistently outperforming conventional approaches and giving outstanding precision, accuracy, and efficiency in the logistics field.

在动态演进的物流领域中，高效沟通是实现运营流程优化的核心前提。我们提出的创新解决方案——多标签集成模型（Multi-Labeling Ensemble，MLEn）——旨在解决多标签数据提取这一复杂任务，通过自然语言工具包（Natural Language Toolkit，NLTK）采用先进技术完成文本数据的精准预处理，且针对物流场景中通用的沟通语言进行了定制化设计。MLEn融合了情感强度分析、词向量模型（Word2Vec）与文档向量模型（Doc2Vec）等创新方法以实现全面的特征提取，尤其适用于电商物流场景，能够捕捉保障高效运营所需的精细化沟通信息。伦理合规是物流沟通的核心基石，MLEn可在检测与分类不当语言方面发挥关键作用，从本质上契合伦理规范要求；其通过词频-逆文档频率（Term Frequency-Inverse Document Frequency，TF-IDF）与瓦尔德情感分析工具（Vader）实现特征增强，能够精准识别并标注物流沟通中的伦理敏感内容。在包括情绪数据集（Emotions）在内的各类公开数据集上，MLEn均能保持92%至97%的优异准确率，彰显了其在物流场景中的性能优势。尤为值得一提的是，我们提出的DenseNet-EHO模型较双向编码器表征模型（Bidirectional Encoder Representations from Transformers，BERT）性能提升8%，较其他同类方法效率提升15%至25%。本次研究通过精确率（precision）、召回率（recall）、F1值（F1-score）、排序损失（Ranking Loss）、雅卡尔相似度（Jaccard Similarity）、受试者工作特征曲线下面积（Area Under Receiver Operating Characteristic Curve，AUC-ROC）、灵敏度（sensitivity）及时序复杂度（time complexity）等多项指标开展全面分析，结果证实DenseNet-EHO的性能效率契合物流场景的实际需求。本研究对于提升物流领域面向方面的情感分析的精确性、多样性与计算效率具有重要价值，通过融合前沿预处理技术、情感强度分析与向量化方法，MLEn成为适配多标签数据集的稳健框架，在物流领域中持续优于传统方法，并展现出卓越的精确性、准确率与运行效率。