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Syntactic- and morphology-based text augmentation framework for Arabic sentiment analysis.

Rehab Duwairi1, Ftoon Abushaqra2

  • 1Department of Computer Information Systems, Jordan University of Science and Technology, Irbid, Jordan.

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|May 6, 2021
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Summary

This study introduces a novel framework for Arabic text augmentation, overcoming data scarcity for machine learning. The method significantly boosts sentiment analysis accuracy by 42% using rule-based sentence expansion.

Keywords:
Arabic textMorphology-based augmentationNatural language processingSentiment analysisText augmentation

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Artificial Intelligence

Background:

  • Arabic natural language processing (NLP) faces challenges due to dialectal diversity, complex syntax, and limited high-quality annotated datasets.
  • Machine learning and deep learning models require extensive data for effective training, a resource scarce for Arabic text.

Purpose of the Study:

  • To present an intelligent framework for augmenting Arabic sentences to address the scarcity of training data.
  • To enhance the performance of Arabic sentiment analysis models through data augmentation.

Main Methods:

  • Developed a novel text augmentation framework leveraging Arabic's rich morphology, synonymy lists, and grammatical/negation rules.
  • Generated new Arabic sentences with preserved sentiment labels from an initial seed dataset.
  • Applied the framework to expand seed datasets by a factor of 10.

Main Results:

  • Achieved a 10-fold increase in the size of the initial Arabic text datasets.
  • Demonstrated a significant 42% average increase in sentiment analysis accuracy using the augmented data.
  • Validated the effectiveness of rule-based augmentation for Arabic NLP tasks.

Conclusions:

  • The proposed framework offers an effective solution for Arabic text data augmentation, a critical step for advancing Arabic NLP.
  • This work represents the first targeted approach to text augmentation specifically for the Arabic language.
  • The high accuracy improvement underscores the reliability and quality of the rule-based augmentation strategy.