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Arab2Vec: An Arabic word embedding model for use in Twitter NLP applications.

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Area of Science:

  • Natural Language Processing (NLP)
  • Machine Learning (ML)
  • Computational Linguistics

Background:

  • Arabic Twitter data analysis is crucial for understanding public sentiment, especially post-COVID-19.
  • Word embedding models are essential for transforming text data into numerical formats for ML algorithms.
  • Existing Arabic word embedding models have limitations in scope and functionality.

Purpose of the Study:

  • To introduce Arab2Vec, a novel and up-to-date word embedding model specifically designed for Arabic Twitter data.
  • To enhance natural language processing applications on Arabic social media.
  • To provide a superior alternative to existing Arabic word embedding models.

Main Methods:

  • Construction of Arab2Vec using a large dataset of approximately 186 million Arabic tweets (2008-2021).
  • Implementation of skip-grams with negative sampling, a novel approach for Arabic models.
  • Development of nine distinct Arab2Vec model versions with varying features and training parameters.

Main Results:

  • Arab2Vec demonstrates superior performance compared to existing models in terms of recognized words and F1 score for classification tasks.
  • The model exhibits effective handling of emojis within Arabic text.
  • Validation through qualitative and quantitative experiments confirms the model's efficacy.

Conclusions:

  • Arab2Vec represents a significant advancement in Arabic word embedding models for Twitter data.
  • The open-source release of Arab2Vec facilitates further research and application in NLP.
  • The model's enhanced capabilities offer improved insights into Arabic social media discourse.