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Updated: Jun 25, 2025

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Enhancing aspect-based multi-labeling with ensemble learning for ethical logistics.

Abdulwahab Ali Almazroi1, Nasir Ayub2

  • 1Department of Information Technology, College of Computing and Information Technology at Khulais, University of Jeddah, Jeddah, Saudi Arabia.

Plos One
|May 21, 2024
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Summary
This summary is machine-generated.

The Multi-Labeling Ensemble (MLEn) enhances logistics communication by accurately extracting multi-labeled data using advanced NLP techniques. This system improves efficiency and ethical language detection in e-commerce logistics.

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Area of Science:

  • Natural Language Processing (NLP)
  • Computational Linguistics
  • Data Science

Background:

  • Effective communication is crucial for streamlined operations in the logistics sector.
  • Extracting multi-labeled data from logistics communication presents significant challenges.
  • Existing methods lack the precision and efficiency required for nuanced logistics data.

Purpose of the Study:

  • To introduce the Multi-Labeling Ensemble (MLEn) for accurate multi-labeled data extraction in logistics.
  • To enhance the processing of textual data specific to logistics communication.
  • To improve ethical language detection and sentiment analysis within the logistics domain.

Main Methods:

  • Utilized Natural Language Toolkit (NLTK) for text preprocessing.
  • Employed sentiment intensity analysis, Word2Vec, and Doc2Vec for feature extraction.
  • Leveraged Tf-IDF and Vader for feature enhancement and ethical content labeling.

Main Results:

  • MLEn achieved accuracy levels of 92%–97% across diverse datasets.
  • The proposed DenseNet-EHO method outperformed BERT by 8% and other techniques by 15-25% in efficiency.
  • Demonstrated superior precision, recall, F1-score, and computational efficiency.

Conclusions:

  • MLEn provides a robust framework for multi-label datasets in logistics.
  • The system significantly enhances precision, diversity, and computational efficiency in aspect-based sentiment analysis.
  • DenseNet-EHO offers a state-of-the-art solution for logistics communication analysis.