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A powerful comparison of deep learning frameworks for Arabic sentiment analysis

Bibliographic Details
Title: A powerful comparison of deep learning frameworks for Arabic sentiment analysis
Authors: Youssra Zahidi, Yacine El Younoussi, Yassine Al-Amrani
Publisher Information: Zenodo
Publication Year: 2021
Collection: Zenodo
Subject Terms: ANLP, ASA, DL, Java libraries, Python libraries
Description: 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.
Document Type: article in journal/newspaper
Language: unknown
Relation: https://zenodo.org/records/4638329; oai:zenodo.org:4638329
DOI: 10.11591/ijece.v11i1.pp745-752
Availability: https://doi.org/10.11591/ijece.v11i1.pp745-752
https://zenodo.org/records/4638329
Rights: Creative Commons Attribution 4.0 International ; cc-by-4.0 ; https://creativecommons.org/licenses/by/4.0/legalcode
Accession Number: edsbas.991AF15B
Database: BASE
FullText Text:
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  Data: A powerful comparison of deep learning frameworks for Arabic sentiment analysis
– Name: Author
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  Data: <searchLink fieldCode="AR" term="%22Youssra+Zahidi%22">Youssra Zahidi</searchLink><br /><searchLink fieldCode="AR" term="%22Yacine+El+Younoussi%22">Yacine El Younoussi</searchLink><br /><searchLink fieldCode="AR" term="%22Yassine+Al-Amrani%22">Yassine Al-Amrani</searchLink>
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  Data: Zenodo
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  Label: Publication Year
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  Data: 2021
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  Data: <searchLink fieldCode="DE" term="%22ANLP%22">ANLP</searchLink><br /><searchLink fieldCode="DE" term="%22ASA%22">ASA</searchLink><br /><searchLink fieldCode="DE" term="%22DL%22">DL</searchLink><br /><searchLink fieldCode="DE" term="%22Java+libraries%22">Java libraries</searchLink><br /><searchLink fieldCode="DE" term="%22Python+libraries%22">Python libraries</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: 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.
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  Data: 10.11591/ijece.v11i1.pp745-752
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