Multi-Versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis on Arabic Corpus Article Swipe
Mesfer Al Duhayyim
,
Badriyya B. Al-onazi
,
Jaber S. Alzahrani
,
Hussain Alshahrani
,
Mohamed Ahmed Elfaki
,
Abdullah Mohamed
,
Ishfaq Yaseen
,
Mohammed Aljebreen
,
Mohammed Rizwanullah
,
Abu Sarwar Zamani
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.32604/csse.2023.033836
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.32604/csse.2023.033836
Sentiment analysis (SA) of the Arabic language becomes important despite scarce annotated corpora and confined sources. Arabic affect Analysis has become an active research zone nowadays. But still, the Arabic language lags behind adequate language sources for enabling the SA tasks. Thus, Arabic still faces challenges in natural language processing (NLP) tasks because of its structure complexities, history, and distinct cultures. It has gained lesser effort than the other languages. This paper developed a Multi-versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis (MVODRL-AA) on Arabic Corpus. The presented MVODRL-AA model majorly concentrates on identifying and classifying effects or emotions that occurred in the Arabic corpus. Firstly, the MVODRL-AA model follows data pre-processing and word embedding. Next, an n-gram model is utilized to generate word embeddings. A deep Q-learning network (DQLN) model is then exploited to identify and classify the effect on the Arabic corpus. At last, the MVO algorithm is used as a hyperparameter tuning approach to adjust the hyperparameters related to the DQLN model, showing the novelty of the work. A series of simulations were carried out to exhibit the promising performance of the MVODRL-AA model. The simulation outcomes illustrate the betterment of the MVODRL-AA method over the other approaches with an accuracy of 99.27%.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.32604/csse.2023.033836
- https://file.techscience.com/files/csse/2023/TSP_CSSE-47-3/TSP_CSSE_33836/TSP_CSSE_33836.pdf
- OA Status
- diamond
- Cited By
- 4
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388552646
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4388552646Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.32604/csse.2023.033836Digital Object Identifier
- Title
-
Multi-Versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis on Arabic CorpusWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
Mesfer Al Duhayyim, Badriyya B. Al-onazi, Jaber S. Alzahrani, Hussain Alshahrani, Mohamed Ahmed Elfaki, Abdullah Mohamed, Ishfaq Yaseen, Mohammed Aljebreen, Mohammed Rizwanullah, Abu Sarwar ZamaniList of authors in order
- Landing page
-
https://doi.org/10.32604/csse.2023.033836Publisher landing page
- PDF URL
-
https://file.techscience.com/files/csse/2023/TSP_CSSE-47-3/TSP_CSSE_33836/TSP_CSSE_33836.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://file.techscience.com/files/csse/2023/TSP_CSSE-47-3/TSP_CSSE_33836/TSP_CSSE_33836.pdfDirect OA link when available
- Concepts
-
Computer science, Natural language processing, Artificial intelligence, Hyperparameter, Word embedding, Reinforcement learning, Novelty, Affect (linguistics), Word (group theory), Arabic, Sentiment analysis, Embedding, Linguistics, Theology, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 2Per-year citation counts (last 5 years)
- References (count)
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23Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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