---
title: "Who cites A Light Lexicon-based Mobile — 5 citing papers"
description: "5 papers of the Elamur Research library cite «A Light Lexicon-based Mobile Application for Sentiment Mining of Arabic Tweets», found in their…"
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---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [A Light Lexicon-based Mobile Application for…](https://md.research.elamur.ai/free/en/papers/badaro-2015-light-lexicon-based-mobile-application/) / cited by

# Who cites «A Light Lexicon-based Mobile Application for Sentiment Mining of Arabic Tweets»

5 papers of our library cite it (of the 149,439 whose bibliographies we read) · 5 with a page here, the most cited first · [← back to «A Light Lexicon-based Mobile Application for Sentiment Mining of Arabic Tweets»](https://md.research.elamur.ai/free/en/papers/badaro-2015-light-lexicon-based-mobile-application/)

1. [**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/)
2. **[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*
3. **[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*
4. **[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*
5. [**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/)
- [← back to](https://md.research.elamur.ai/free/en/papers/badaro-2015-light-lexicon-based-mobile-application/) — A Light Lexicon-based Mobile Application for Sentiment Mining of Arabic Tweets