🔗 Who cites «SemEval-2016 Task 7: Determining Sentiment Intensity of English and Arabic Phrases»

35 papers of our library cite it (of the 149,439 whose bibliographies we read) · 16 with a page here, the most cited first · ← back to «SemEval-2016 Task 7: Determining Sentiment Intensity of English and Arabic Phrases»

1. SemEval-2017 Task 4: Sentiment Analysis in TwitterInternational Workshop on Semantic Evaluation (SemEval)Sara Rosenthal, Noura Farra, Preslav Nakov2017 · 1,163 citations
2. ARBERT & MARBERT: Deep Bidirectional Transformers for ArabicarXiv:2101.01785 · ACL-2021 camera ready versionMuhammad Abdul-Mageed, AbdelRahim Elmadany, El Moatez Billah Nagoudi2021 · 478 citations
3. Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best–Worst ScalingConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Svetlana Kiritchenko, Saif M. Mohammad2016 · 181 citations
4. From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm DatasetWorkshop on Open-Source Arabic Corpora and Processing Tools with Shared TasksIbrahim Abu Farha, Walid Magdy2020 · 132 citations
5. Overview of the WANLP 2021 Shared Task on Sarcasm and Sentiment Detection in ArabicArabic Natural Language Processing Workshop (WANLP)Ibrahim Abu Farha, Wajdi Zaghouani, Walid Magdy2021 · 112 citations
6. Sentiment Lexicons for Arabic Social MediaLanguage Resources and Evaluation Conference (LREC)Saif Mohammad, Mohammad Salameh, Svetlana Kiritchenko2016 · 83 citations
7. TeamUNCC at SemEval-2018 Task 1: Emotion Detection in English and Arabic Tweets using Deep LearningInternational Workshop on Semantic Evaluation (SemEval)Malak Abdullah, Samira Shaikh2018 · 68 citations
8. Improving Sentiment Analysis in Arabic Using Word RepresentationarXiv:1803.00124 · Proc. 2nd International Workshop on Arabic and Derived Script Analysis and Recognition (ASAR '18), pp. 13-18. IEEE, 2018Abdulaziz M. Alayba, Vasile Palade, Matthew England, et al.2018 · 59 citations
9. iLab-Edinburgh at SemEval-2016 Task 7: A Hybrid Approach for Determining Sentiment Intensity of Arabic Twitter PhrasesInternational Workshop on Semantic Evaluation (SemEval)Eshrag Refaee, Verena Rieser2016 · 32 citations
10. AraNet: A Deep Learning Toolkit for Arabic Social MediaWorkshop on Open-Source Arabic Corpora and Processing Tools with Shared TasksMuhammad Abdul-Mageed, Chiyu Zhang, Azadeh Hashemi, et al.2020 · 25 citations
11. Sentiment analysis for Arabic language: A brief survey of approaches and techniquesarXiv:1809.02782Mo'ath Alrefai, Hossam Faris, Ibrahim Aljarah2018 · 24 citations
12. AlclaM: Arabic Dialect Language ModelArabic Natural Language Processing Conference (ArabicNLP)Murtadha Ahmed, Saghir Alfasly, Bo Wen, et al.2024 · 10 citations
13. Combining Context-Free and Contextualized Representations for Arabic Sarcasm Detection and Sentiment IdentificationArabic Natural Language Processing Workshop (WANLP)Amey Hengle, Atharva Kshirsagar, Shaily Desai, et al.2021 · 6 citations
14. SentiArabic: A Sentiment Analyzer for Standard ArabicLanguage Resources and Evaluation Conference (LREC)Ramy Eskander2018 · 4 citations
15. ARB-SEN at SemEval-2018 Task1: A New Set of Features for Enhancing the Sentiment Intensity Prediction in Arabic TweetsInternational Workshop on Semantic Evaluation (SemEval)El Moatez Billah Nagoudi2018 · 3 citations
16. AlcLaM: Arabic Dialectal Language ModelarXiv:2407.13097Murtadha Ahmed, Saghir Alfasly, Bo Wen, et al.2024 · 1 citation

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