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Quantum computing and machine learning for Arabic language sentiment classification in social media.

Ahmed Omar1, Tarek Abd El-Hafeez2,3

  • 1Department of Computer Science, Faculty of Science, Minia University, EL-Minia, Egypt. ahmed.omar@mu.edu.eg.

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Summary
This summary is machine-generated.

Quantum computing and machine learning show high accuracy for Arabic document classification. Quantum computing slightly outperforms classic ML in accuracy and speed on large datasets, while ML is faster on smaller ones.

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Quantum Computing

Background:

  • The growing volume of Arabic digital data necessitates advanced document classification.
  • Quantum computing and machine learning (ML) are promising for document classification.
  • Research on these techniques for Arabic language data is limited.

Purpose of the Study:

  • To compare the performance of quantum computing and classic machine learning for Arabic document classification.
  • To evaluate accuracy, precision, recall, and F1 scores for both approaches.
  • To analyze processing times on datasets of varying sizes.

Main Methods:

  • Comparative analysis of quantum computing and classic ML algorithms.
  • Utilized two datasets of Arabic tweets for sentiment analysis.
  • Evaluated performance using metrics like accuracy, precision, recall, and F1 score.

Main Results:

  • Both quantum computing and ML achieved high accuracy in Arabic sentiment analysis.
  • Quantum computing slightly outperformed ML in accuracy and speed on a large dataset (213,465 tweets).
  • On a smaller dataset (44,000 tweets), ML showed higher accuracy, with similar processing times.

Conclusions:

  • Quantum computing is effective for high-accuracy Arabic sentiment analysis on large datasets.
  • Classic ML offers faster processing for smaller Arabic datasets.
  • This study highlights the potential of quantum computing for Arabic document classification challenges.