A powerful comparison of deep learning frameworks for Arabic sentiment analysis

Deep learning (DL) is a machine learning (ML) subdomain that involves algorithms taken from the brain function named artificial neural networks (ANNs). Recently, DL approaches have gained major accomplishments across various Arabic natural language processing (ANLP) tasks, especially in the domain of Arabic sentiment analysis (ASA). For working on Arabic SA, researchers can use various DL libraries in their projects, but without justifying their choice or they choose a group of libraries relying on their particular programming language familiarity. We are basing in this work on Java and Python programming languages because they have a large set of deep learning libraries that are very useful in the ASA domain. This paper focuses on a comparative analysis of different valuable Python and Java libraries to conclude the most relevant and robust DL libraries for ASA. Throw this comparative analysis, and we find that: TensorFlow, Theano, and Keras Python frameworks are very popular and very used in this research domain.

• Publication year

  2021

• Venue

  International Journal of Electrical and Computer Engineering

• Publication date

  2021-02-01

• Fields of study

  Linguistics, Computer Science

• Identifiers
  DOI 10.11591/IJECE.V11I1.PP745-752
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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