Arabic Sentiment Analysis of YouTube Comments: NLP-Based Machine Learning Approaches for Content Evaluation Article Swipe
Dhiaa Musleh
,
Ibrahim Alkhwaja
,
Ali Alkhwaja
,
Mohammed Alghamdi
,
Hussam Abahussain
,
Faisal Alfawaz
,
Nasro Min‐Allah
,
Mamoun Masoud Abdulqader
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/bdcc7030127
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/bdcc7030127
YouTube is a popular video-sharing platform that offers a diverse range of content. Assessing the quality of a video without watching it poses a significant challenge, especially considering the recent removal of the dislike count feature on YouTube. Although comments have the potential to provide insights into video content quality, navigating through the comments section can be time-consuming and overwhelming work for both content creators and viewers. This paper proposes an NLP-based model to classify Arabic comments as positive or negative. It was trained on a novel dataset of 4212 labeled comments, with a Kappa score of 0.818. The model uses six classifiers: SVM, Naïve Bayes, Logistic Regression, KNN, Decision Tree, and Random Forest. It achieved 94.62% accuracy and an MCC score of 91.46% with NB. Precision, Recall, and F1-measure for NB were 94.64%, 94.64%, and 94.62%, respectively. The Decision Tree had a suboptimal performance with 84.10% accuracy and an MCC score of 69.64% without TF-IDF. This study provides valuable insights for content creators to improve their content and audience engagement by analyzing viewers’ sentiments toward the videos. Furthermore, it bridges a literature gap by offering a comprehensive approach to Arabic sentiment analysis, which is currently limited in the field.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/bdcc7030127
- https://www.mdpi.com/2504-2289/7/3/127/pdf?version=1688374192
- OA Status
- gold
- Cited By
- 34
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4383265687
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4383265687Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/bdcc7030127Digital Object Identifier
- Title
-
Arabic Sentiment Analysis of YouTube Comments: NLP-Based Machine Learning Approaches for Content EvaluationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-07-03Full publication date if available
- Authors
-
Dhiaa Musleh, Ibrahim Alkhwaja, Ali Alkhwaja, Mohammed Alghamdi, Hussam Abahussain, Faisal Alfawaz, Nasro Min‐Allah, Mamoun Masoud AbdulqaderList of authors in order
- Landing page
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https://doi.org/10.3390/bdcc7030127Publisher landing page
- PDF URL
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https://www.mdpi.com/2504-2289/7/3/127/pdf?version=1688374192Direct link to full text PDF
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-
YesWhether a free full text is available
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-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2504-2289/7/3/127/pdf?version=1688374192Direct OA link when available
- Concepts
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Computer science, Naive Bayes classifier, Decision tree, Artificial intelligence, Random forest, Support vector machine, Sentiment analysis, Machine learning, Natural language processing, Information retrievalTop concepts (fields/topics) attached by OpenAlex
- Cited by
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34Total citation count in OpenAlex
- Citations by year (recent)
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2025: 12, 2024: 22Per-year citation counts (last 5 years)
- References (count)
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36Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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