--- title: "Who cites A Characterization Study of — 11 citing papers" description: "11 papers of the Elamur Research library cite «A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art…" canonical: "https://research.elamur.ai/free/en/papers/baly-2017-characterization-study-arabic-twitter-data/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/baly-2017-characterization-study-arabic-twitter-data/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [A Characterization Study of Arabic Twitter Data…](https://md.research.elamur.ai/free/en/papers/baly-2017-characterization-study-arabic-twitter-data/) / cited by # Who cites «A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models» 11 papers of our library cite it (of the 149,439 whose bibliographies we read) · 11 with a page here, the most cited first · [← back to «A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models»](https://md.research.elamur.ai/free/en/papers/baly-2017-characterization-study-arabic-twitter-data/) 1. **[EMA at SemEval-2018 Task 1: Emotion Mining for Arabic](https://md.research.elamur.ai/free/en/papers/badaro-2018-ema-semeval-2018-task-1/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Gilbert Badaro, Obeida El Jundi, Alaa Khaddaj, et al.*2018 · 73 citations* 2. [**ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets arXiv:1906.01830**Ramy Baly, Alaa Khaddaj, Hazem Hajj, et al.*2019 · 61 citations*](https://md.research.elamur.ai/free/en/papers/baly-2019-arsentd-lev-multi-topic-corpus/) 3. **[EmoWordNet: Automatic Expansion of Emotion Lexicon Using English WordNet](https://md.research.elamur.ai/free/en/papers/badaro-2018-emowordnet-automatic-expansion-emotion-lexicon/) [Joint Conference on Lexical and Computational Semantics (*SEM)](https://md.research.elamur.ai/free/en/venues/starsem/)**Gilbert Badaro, Hussein Jundi, Hazem Hajj, et al.*2018 · 43 citations* 4. **[Combining BERT with Static Word Embeddings for Categorizing Social Media](https://md.research.elamur.ai/free/en/papers/alghanmi-2020-combining-bert-static-word-embeddings/) [Workshop on Noisy User-generated Text](https://md.research.elamur.ai/free/en/venues/wnut/)**Israa Alghanmi, Luis Espinosa Anke, Steven Schockaert*2020 · 40 citations* 5. **[Empirical evaluation of shallow and deep learning classifiers for Arabic sentiment analysis](https://md.research.elamur.ai/free/en/papers/nassif-2021-empirical-evaluation-shallow-deep-learning/) [ACM Transactions on Asian and Low-Resource Language Information Processing](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-asian-and-low-resource-language-information-processing/)**Ali Bou Nassif, Abdollah Masoud Darya, Ashraf Elnagar*2021 · 34 citations* 6. **[OMAM at SemEval-2017 Task 4: Evaluation of English State-of-the-Art Sentiment Analysis Models for Arabic and a New Topic-based Model](https://md.research.elamur.ai/free/en/papers/baly-2017-omam-semeval-2017-task-4/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Ramy Baly, Gilbert Badaro, Ali Hamdi, et al.*2017 · 33 citations* 7. **[Improved Generalization of Arabic Text Classifiers](https://md.research.elamur.ai/free/en/papers/khaddaj-2019-improved-generalization-arabic-text-classifiers/) [Arabic Natural Language Processing Workshop (WANLP)](https://md.research.elamur.ai/free/en/venues/wanlp/)**Alaa Khaddaj, Hazem Hajj, Wassim El-Hajj*2019 · 10 citations* 8. **[A Comprehensive Survey of Contemporary Arabic Sentiment Analysis: Methods, Challenges, and Future Directions](https://md.research.elamur.ai/free/en/papers/shi-2025-comprehensive-survey-contemporary-arabic-sentiment/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhiqiang Shi, Ruchit Agrawal*2025 · 9 citations* 9. **[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* 10. **[Can Modern Standard Arabic Approaches be used for Arabic Dialects? Sentiment Analysis as a Case Study](https://md.research.elamur.ai/free/en/papers/kwaik-2019-can-modern-standard-arabic-approaches/) [Workshop on Arabic Corpus Linguistics](https://md.research.elamur.ai/free/en/venues/wacl/)**Kathrein Abu Kwaik, Stergios Chatzikyriakidis, Simon Dobnik*2019 · 6 citations* 11. [**Sentiment Analysis in Poems in Misurata Sub-dialect — A Sentiment Detection in an Arabic Sub-dialect arXiv:2109.07203**Azza Abugharsa*2021*](https://md.research.elamur.ai/free/en/papers/abugharsa-2021-sentiment-analysis-poems-misurata-sub/) - [← back to](https://md.research.elamur.ai/free/en/papers/baly-2017-characterization-study-arabic-twitter-data/) — A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models