Enhancing Arabic Sentiment Analysis Through a Hybrid Deep Learning Approach

Mustafa Mhamed; Jamal Ali Noja

doi:doi:10.11648/j.ijecs.20230804.15

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Enhancing Arabic Sentiment Analysis Through a Hybrid Deep Learning Approach

Mustafa Mhamed, Jamal Ali Noja

Published in International Journal of Education, Culture and Society (Volume 8, Issue 4)

Received: 15 June 2023 Accepted: 26 July 2023 Published: 31 July 2023

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Abstract

Sentiment analysis is a key procedure in many natural language processing systems that extract emotions from textual input. In recent years, Arabic sentiment analysis has become a significant study area. With the growth of social media platforms and data flow, especially in Arabic, substantial difficulties have emerged that call for new strategies to address problems, such as the Arabic language's complicated development and the complexity of the multiple, binary, or massively imbalanced Arabic dataset categorizations. Besides, the system's limitations, whether in online analysis tools, deep or machine learning. This paper proposes a new conjunction method for Arabic sentiment analysis (ASA) called Hybrid Convolution Gate Long (HCGL). This method allows us to extract the best features, handle sequences of different lengths to capture context, address the issue of disappearing error gradients, and improve prediction performance. To match other research works, we conduct studies using a variety of data splits. Furthermore, we pay great attention to Arabic preparation by using all-encompassing procedures that help us address the Arabic language context. The proposed method performs highest in terms of 2-class way efficiency (95.88%), followed by 3-class way performance (95.92%). Additionally, we apply it to the massive Arabic sentiment dataset; it performs well, achieving 88.40% in less time.

Published in	International Journal of Education, Culture and Society (Volume 8, Issue 4)
DOI	10.11648/j.ijecs.20230804.15
Page(s)	183-189
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Deep Learning, Natural Language Processing (NLP), Hybrid Convolution Gate Long (HCGL), Arabic Sentiment Analysis (ASA)

References

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APA Style

Mustafa Mhamed, Jamal Ali Noja. (2023). Enhancing Arabic Sentiment Analysis Through a Hybrid Deep Learning Approach. International Journal of Education, Culture and Society, 8(4), 183-189. https://doi.org/10.11648/j.ijecs.20230804.15

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ACS Style

Mustafa Mhamed; Jamal Ali Noja. Enhancing Arabic Sentiment Analysis Through a Hybrid Deep Learning Approach. Int. J. Educ. Cult. Soc. 2023, 8(4), 183-189. doi: 10.11648/j.ijecs.20230804.15

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AMA Style

Mustafa Mhamed, Jamal Ali Noja. Enhancing Arabic Sentiment Analysis Through a Hybrid Deep Learning Approach. Int J Educ Cult Soc. 2023;8(4):183-189. doi: 10.11648/j.ijecs.20230804.15

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@article{10.11648/j.ijecs.20230804.15,
  author = {Mustafa Mhamed and Jamal Ali Noja},
  title = {Enhancing Arabic Sentiment Analysis Through a Hybrid Deep Learning Approach},
  journal = {International Journal of Education, Culture and Society},
  volume = {8},
  number = {4},
  pages = {183-189},
  doi = {10.11648/j.ijecs.20230804.15},
  url = {https://doi.org/10.11648/j.ijecs.20230804.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijecs.20230804.15},
  abstract = {Sentiment analysis is a key procedure in many natural language processing systems that extract emotions from textual input. In recent years, Arabic sentiment analysis has become a significant study area. With the growth of social media platforms and data flow, especially in Arabic, substantial difficulties have emerged that call for new strategies to address problems, such as the Arabic language's complicated development and the complexity of the multiple, binary, or massively imbalanced Arabic dataset categorizations. Besides, the system's limitations, whether in online analysis tools, deep or machine learning. This paper proposes a new conjunction method for Arabic sentiment analysis (ASA) called Hybrid Convolution Gate Long (HCGL). This method allows us to extract the best features, handle sequences of different lengths to capture context, address the issue of disappearing error gradients, and improve prediction performance. To match other research works, we conduct studies using a variety of data splits. Furthermore, we pay great attention to Arabic preparation by using all-encompassing procedures that help us address the Arabic language context. The proposed method performs highest in terms of 2-class way efficiency (95.88%), followed by 3-class way performance (95.92%). Additionally, we apply it to the massive Arabic sentiment dataset; it performs well, achieving 88.40% in less time.},
 year = {2023}
}

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TY  - JOUR
T1  - Enhancing Arabic Sentiment Analysis Through a Hybrid Deep Learning Approach
AU  - Mustafa Mhamed
AU  - Jamal Ali Noja
Y1  - 2023/07/31
PY  - 2023
N1  - https://doi.org/10.11648/j.ijecs.20230804.15
DO  - 10.11648/j.ijecs.20230804.15
T2  - International Journal of Education, Culture and Society
JF  - International Journal of Education, Culture and Society
JO  - International Journal of Education, Culture and Society
SP  - 183
EP  - 189
PB  - Science Publishing Group
SN  - 2575-3363
UR  - https://doi.org/10.11648/j.ijecs.20230804.15
AB  - Sentiment analysis is a key procedure in many natural language processing systems that extract emotions from textual input. In recent years, Arabic sentiment analysis has become a significant study area. With the growth of social media platforms and data flow, especially in Arabic, substantial difficulties have emerged that call for new strategies to address problems, such as the Arabic language's complicated development and the complexity of the multiple, binary, or massively imbalanced Arabic dataset categorizations. Besides, the system's limitations, whether in online analysis tools, deep or machine learning. This paper proposes a new conjunction method for Arabic sentiment analysis (ASA) called Hybrid Convolution Gate Long (HCGL). This method allows us to extract the best features, handle sequences of different lengths to capture context, address the issue of disappearing error gradients, and improve prediction performance. To match other research works, we conduct studies using a variety of data splits. Furthermore, we pay great attention to Arabic preparation by using all-encompassing procedures that help us address the Arabic language context. The proposed method performs highest in terms of 2-class way efficiency (95.88%), followed by 3-class way performance (95.92%). Additionally, we apply it to the massive Arabic sentiment dataset; it performs well, achieving 88.40% in less time.
VL  - 8
IS  - 4
ER  -

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Author Information

Mustafa Mhamed

School of Information Science and Technology, Northwest University, Xi'an, China
Jamal Ali Noja

College of Agricultural Sciences, Dalanj University, Dilling, Sudan

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APA Style

Mustafa Mhamed, Jamal Ali Noja. (2023). Enhancing Arabic Sentiment Analysis Through a Hybrid Deep Learning Approach. International Journal of Education, Culture and Society, 8(4), 183-189. https://doi.org/10.11648/j.ijecs.20230804.15

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ACS Style

Mustafa Mhamed; Jamal Ali Noja. Enhancing Arabic Sentiment Analysis Through a Hybrid Deep Learning Approach. Int. J. Educ. Cult. Soc. 2023, 8(4), 183-189. doi: 10.11648/j.ijecs.20230804.15

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AMA Style

Mustafa Mhamed, Jamal Ali Noja. Enhancing Arabic Sentiment Analysis Through a Hybrid Deep Learning Approach. Int J Educ Cult Soc. 2023;8(4):183-189. doi: 10.11648/j.ijecs.20230804.15

Copy | Download

@article{10.11648/j.ijecs.20230804.15,
  author = {Mustafa Mhamed and Jamal Ali Noja},
  title = {Enhancing Arabic Sentiment Analysis Through a Hybrid Deep Learning Approach},
  journal = {International Journal of Education, Culture and Society},
  volume = {8},
  number = {4},
  pages = {183-189},
  doi = {10.11648/j.ijecs.20230804.15},
  url = {https://doi.org/10.11648/j.ijecs.20230804.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijecs.20230804.15},
  abstract = {Sentiment analysis is a key procedure in many natural language processing systems that extract emotions from textual input. In recent years, Arabic sentiment analysis has become a significant study area. With the growth of social media platforms and data flow, especially in Arabic, substantial difficulties have emerged that call for new strategies to address problems, such as the Arabic language's complicated development and the complexity of the multiple, binary, or massively imbalanced Arabic dataset categorizations. Besides, the system's limitations, whether in online analysis tools, deep or machine learning. This paper proposes a new conjunction method for Arabic sentiment analysis (ASA) called Hybrid Convolution Gate Long (HCGL). This method allows us to extract the best features, handle sequences of different lengths to capture context, address the issue of disappearing error gradients, and improve prediction performance. To match other research works, we conduct studies using a variety of data splits. Furthermore, we pay great attention to Arabic preparation by using all-encompassing procedures that help us address the Arabic language context. The proposed method performs highest in terms of 2-class way efficiency (95.88%), followed by 3-class way performance (95.92%). Additionally, we apply it to the massive Arabic sentiment dataset; it performs well, achieving 88.40% in less time.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Enhancing Arabic Sentiment Analysis Through a Hybrid Deep Learning Approach
AU  - Mustafa Mhamed
AU  - Jamal Ali Noja
Y1  - 2023/07/31
PY  - 2023
N1  - https://doi.org/10.11648/j.ijecs.20230804.15
DO  - 10.11648/j.ijecs.20230804.15
T2  - International Journal of Education, Culture and Society
JF  - International Journal of Education, Culture and Society
JO  - International Journal of Education, Culture and Society
SP  - 183
EP  - 189
PB  - Science Publishing Group
SN  - 2575-3363
UR  - https://doi.org/10.11648/j.ijecs.20230804.15
AB  - Sentiment analysis is a key procedure in many natural language processing systems that extract emotions from textual input. In recent years, Arabic sentiment analysis has become a significant study area. With the growth of social media platforms and data flow, especially in Arabic, substantial difficulties have emerged that call for new strategies to address problems, such as the Arabic language's complicated development and the complexity of the multiple, binary, or massively imbalanced Arabic dataset categorizations. Besides, the system's limitations, whether in online analysis tools, deep or machine learning. This paper proposes a new conjunction method for Arabic sentiment analysis (ASA) called Hybrid Convolution Gate Long (HCGL). This method allows us to extract the best features, handle sequences of different lengths to capture context, address the issue of disappearing error gradients, and improve prediction performance. To match other research works, we conduct studies using a variety of data splits. Furthermore, we pay great attention to Arabic preparation by using all-encompassing procedures that help us address the Arabic language context. The proposed method performs highest in terms of 2-class way efficiency (95.88%), followed by 3-class way performance (95.92%). Additionally, we apply it to the massive Arabic sentiment dataset; it performs well, achieving 88.40% in less time.
VL  - 8
IS  - 4
ER  -

Copy | Download