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Position-context additive transformer-based model for classifying text data on social media.

M M Abd-Elaziz1, Nora El-Rashidy2, Ahmed Abou Elfetouh3

  • 1Information Systems Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura, Egypt. moh_abdelaziz7@hotmail.com.

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Summary
This summary is machine-generated.

A new Position-Context Additive transformer model (PCA model) enhances social media text classification accuracy. The PCA model improves word embedding and attention mechanisms, outperforming existing models and showing increased accuracy with more training data.

Keywords:
Additive attentionBi-LSTM networkSocial mediaTransformer-based modelWord embedding

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Social Media Analytics

Background:

  • The proliferation of social media text necessitates advanced classification models.
  • Transformer-based models are effective for natural language processing tasks.
  • Existing models may not fully capture positional and contextual information in social media text.

Purpose of the Study:

  • To introduce a novel Position-Context Additive transformer model (PCA model) for improved social media text classification.
  • To enhance text representation by integrating word position and context.
  • To refine the additive attention mechanism within transformer architectures.

Main Methods:

  • Developed a two-phase approach: Phase I integrates improved word embeddings (position) with Bi-LSTM (context).
  • Phase II enhances transformer models by improving the additive attention mechanism.
  • Evaluated the PCA model on six datasets for health-related social media text classification.

Main Results:

  • The PCA model achieved improved F1-Scores (0.2-10.2%) on five datasets compared to state-of-the-art results.
  • Outperformed three other transformer-based models in four datasets, with F1-score improvements of 0.1-2.1%.
  • Demonstrated a positive correlation between training data volume and performance accuracy.

Conclusions:

  • The PCA model offers superior performance for social media text classification.
  • Integrating positional and contextual information is crucial for enhancing text representation.
  • Larger training datasets positively impact the accuracy of transformer-based text classification models.