Improving Sentiment Analysis of Arabic Tweets by One-way ANOVA
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2022/06/01
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Indian UGC (journal)
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Refrences48
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Citations15
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Manar Alassaf
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Ali Mustafa Qamar
Cite
Alassaf, Manar, and Ali Mustafa Qamar. “Improving Sentiment Analysis of Arabic Tweets by One-Way ANOVA”. Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 6, 2022, pp. 2849-5, https://doi.org/10.1016/j.jksuci.2020.10.023.
Alassaf, M., & Qamar, A. M. (2022). Improving Sentiment Analysis of Arabic Tweets by One-way ANOVA. Journal of King Saud University - Computer and Information Sciences, 34(6), 2849-2859. https://doi.org/10.1016/j.jksuci.2020.10.023
Alassaf M, Qamar AM. Improving Sentiment Analysis of Arabic Tweets by One-way ANOVA. Journal of King Saud University - Computer and Information Sciences. 2022;34(6):2849-5.
Journal Categories
Science
Mathematics
Instruments and machines
Electronic computers
Computer science
Science
Mathematics
Instruments and machines
Electronic computers
Computer science
Computer software
Science
Science (General)
Cybernetics
Information theory
Technology
Electrical engineering
Electronics
Nuclear engineering
Electronics
Computer engineering
Computer hardware
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Towards enhancement of a lexicon-based approach for Saudi dialect sentiment analysis | Journal of Information Science |
| 26 | 2018 |
| Arabic Sentiment Classification using MLP Network Hybrid with Naive Bayes Algorithm | Journal of Computer Science | 5 | 2018 | |
| Sentiment analysis in Arabic: A review of the literature | Ain Shams Engineering Journal |
| 82 | 2018 |
| Sentiment Analysis of Tweets at Sentence Level Using Hadoop | HELIX | 1 | 2018 | |
| The ASA Statement on p-Values: Context, Process, and Purpose | The American Statistician |
| 3,545 | 2016 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Machine learning (ML) for fluvial lithofacies identification from well logs: A hybrid classification model integrating lithofacies characteristics, logging data distributions, and ML models applicability | Geoenergy Science and Engineering | 2024 | ||
| User satisfaction with Arabic COVID-19 apps: Sentiment analysis of users’ reviews using machine learning techniques | Information Processing & Management |
| 1 | 2024 |
| Novel Framework for an Intrusion Detection System Using Multiple Feature Selection Methods Based on Deep Learning | Tsinghua Science and Technology | 2024 | ||
| Regeneration of the Industrially Deactivated Dehydrogenation Catalysts | Catalysis Surveys from Asia |
| 2023 | |
| Collaborative filtering integrated fine-grained sentiment for hybrid recommender system | The Journal of Supercomputing |
| 2023 |
Citations Analysis
The category
Science: Science (General): Cybernetics: Information theory 4
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Aspect-based sentiment analysis: an overview in the use of Arabic language and was published in 2022. The most recent citation comes from a 2024 study titled Novel Framework for an Intrusion Detection System Using Multiple Feature Selection Methods Based on Deep Learning. This article reached its peak citation in 2023, with 9 citations. It has been cited in 15 different journals, 26% of which are open access. Among related journals, the Tsinghua Science and Technology cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.