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AraSenTi-Tweet: A Corpus for Arabic Sentiment Analysis of Saudi Tweets., , , and . ACLING, volume 117 of Procedia Computer Science, page 63-72. Elsevier, (2017)When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards., , , , , , , , , and 2 other author(s). ACL (1), page 13787-13805. Association for Computational Linguistics, (2024)Teaching Research Methods for Computer Science Students using Virtual Learning: A Case Study., , and . CSEDU (1), page 518-522. SCITEPRESS, (2020)Sentiment Analysis of Arabic Tweets: Feature Engineering and A Hybrid Approach., , , and . CoRR, (2018)SUAR: Towards Building a Corpus for the Saudi Dialect., , , , , , , , , and 7 other author(s). ACLING, volume 142 of Procedia Computer Science, page 72-82. Elsevier, (2018)MADAD: A Readability Annotation Tool for Arabic Text., , , , , , and . LREC, European Language Resources Association (ELRA), (2016)Subjectivity and sentiment analysis of Arabic: Trends and challenges., , and . AICCSA, page 148-155. IEEE Computer Society, (2014)ALLaM: Large Language Models for Arabic and English., , , , , , , , , and 15 other author(s). CoRR, (2024)What does BERT Learn from Arabic Machine Reading Comprehension Datasets?, , and . WANLP, page 32-41. Association for Computational Linguistics, (2021)AraSenTi-Lexicon: A Different Approach., , , , , and . HCI (13), volume 10283 of Lecture Notes in Computer Science, page 226-235. Springer, (2017)