--- title: "Who cites SemEval-2016 Task 7: — 35 citing papers" description: "35 papers of the Elamur Research library cite «SemEval-2016 Task 7: Determining Sentiment Intensity of English and Arabic Phrases»…" canonical: "https://research.elamur.ai/free/en/papers/kiritchenko-2016-semeval-2016-task-7-determining/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/kiritchenko-2016-semeval-2016-task-7-determining/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [SemEval-2016 Task 7: Determining Sentiment…](https://md.research.elamur.ai/free/en/papers/kiritchenko-2016-semeval-2016-task-7-determining/) / cited by # 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»](https://md.research.elamur.ai/free/en/papers/kiritchenko-2016-semeval-2016-task-7-determining/) 1. **[SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://md.research.elamur.ai/free/en/papers/rosenthal-2017-semeval-2017-task-4-sentiment/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Sara Rosenthal, Noura Farra, Preslav Nakov*2017 · 1,163 citations* 2. [**ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic arXiv:2101.01785 · ACL-2021 camera ready version**Muhammad Abdul-Mageed, AbdelRahim Elmadany, El Moatez Billah Nagoudi*2021 · 478 citations*](https://md.research.elamur.ai/free/en/papers/abdul-mageed-2021-arbert-marbert-deep-bidirectional-transformers/) 3. **[Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best–Worst Scaling](https://md.research.elamur.ai/free/en/papers/kiritchenko-2016-capturing-reliable-fine-grained-sentiment/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Svetlana Kiritchenko, Saif M. Mohammad*2016 · 181 citations* 4. **[From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset](https://md.research.elamur.ai/free/en/papers/farha-2020-arabic-sentiment-analysis-sarcasm-detection/) [Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks](https://md.research.elamur.ai/free/en/venues/osact/)**Ibrahim Abu Farha, Walid Magdy*2020 · 132 citations* 5. **[Overview of the WANLP 2021 Shared Task on Sarcasm and Sentiment Detection in Arabic](https://md.research.elamur.ai/free/en/papers/farha-2021-overview-wanlp-2021-shared-task/) [Arabic Natural Language Processing Workshop (WANLP)](https://md.research.elamur.ai/free/en/venues/wanlp/)**Ibrahim Abu Farha, Wajdi Zaghouani, Walid Magdy*2021 · 112 citations* 6. **[Sentiment Lexicons for Arabic Social Media](https://md.research.elamur.ai/free/en/papers/mohammad-2016-sentiment-lexicons-arabic-social-media/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Saif Mohammad, Mohammad Salameh, Svetlana Kiritchenko*2016 · 83 citations* 7. **[TeamUNCC at SemEval-2018 Task 1: Emotion Detection in English and Arabic Tweets using Deep Learning](https://md.research.elamur.ai/free/en/papers/abdullah-2018-teamuncc-semeval-2018-task-1/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Malak Abdullah, Samira Shaikh*2018 · 68 citations* 8. [**Improving Sentiment Analysis in Arabic Using Word Representation arXiv:1803.00124 · Proc. 2nd International Workshop on Arabic and Derived Script Analysis and Recognition (ASAR '18), pp. 13-18. IEEE, 2018**Abdulaziz M. Alayba, Vasile Palade, Matthew England, et al.*2018 · 59 citations*](https://md.research.elamur.ai/free/en/papers/alayba-2018-improving-sentiment-analysis-arabic-using/) 9. **[iLab-Edinburgh at SemEval-2016 Task 7: A Hybrid Approach for Determining Sentiment Intensity of Arabic Twitter Phrases](https://md.research.elamur.ai/free/en/papers/refaee-2016-ilab-edinburgh-semeval-2016-task/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Eshrag Refaee, Verena Rieser*2016 · 32 citations* 10. **[AraNet: A Deep Learning Toolkit for Arabic Social Media](https://md.research.elamur.ai/free/en/papers/mageed-2020-aranet-deep-learning-toolkit-arabic/) [Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks](https://md.research.elamur.ai/free/en/venues/osact/)**Muhammad Abdul-Mageed, Chiyu Zhang, Azadeh Hashemi, et al.*2020 · 25 citations* 11. [**Sentiment analysis for Arabic language: A brief survey of approaches and techniques arXiv:1809.02782**Mo'ath Alrefai, Hossam Faris, Ibrahim Aljarah*2018 · 24 citations*](https://md.research.elamur.ai/free/en/papers/alrefai-2018-sentiment-analysis-arabic-language-brief/) 12. **[AlclaM: Arabic Dialect Language Model](https://md.research.elamur.ai/free/en/papers/ahmed-2024-alclam-arabic-dialect-language-model/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/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 Identification](https://md.research.elamur.ai/free/en/papers/hengle-2021-combining-context-free-contextualized-representations/) [Arabic Natural Language Processing Workshop (WANLP)](https://md.research.elamur.ai/free/en/venues/wanlp/)**Amey Hengle, Atharva Kshirsagar, Shaily Desai, et al.*2021 · 6 citations* 14. **[SentiArabic: A Sentiment Analyzer for Standard Arabic](https://md.research.elamur.ai/free/en/papers/eskander-2018-sentiarabic-sentiment-analyzer-standard-arabic/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Ramy Eskander*2018 · 4 citations* 15. **[ARB-SEN at SemEval-2018 Task1: A New Set of Features for Enhancing the Sentiment Intensity Prediction in Arabic Tweets](https://md.research.elamur.ai/free/en/papers/nagoudi-2018-arb-sen-semeval-2018-task1/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**El Moatez Billah Nagoudi*2018 · 3 citations* 16. [**AlcLaM: Arabic Dialectal Language Model arXiv:2407.13097**Murtadha Ahmed, Saghir Alfasly, Bo Wen, et al.*2024 · 1 citation*](https://md.research.elamur.ai/free/en/papers/ahmed-2024-alclam-arabic-dialectal-language-model/) 19 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/kiritchenko-2016-semeval-2016-task-7-determining/) — SemEval-2016 Task 7: Determining Sentiment Intensity of English and Arabic Phrases