---
title: "LEBANONUPRISING: a thorough study of Lebanese tweets (2020)"
description: "The paper presents what the authors describe as the first sentiment study of tweets in spoken Lebanese Arabic, a dialect with… 16 references."
authors: ["Reda Khalaf", "Mireille Makary"]
year: 2020
venue: "arXiv:2009.14459"
source: "https://arxiv.org/abs/2009.14459"
doi: "10.48550/arxiv.2009.14459"
arxiv: "2009.14459"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/khalaf-2020-lebanonuprising-thorough-study-lebanese-tweets/"
---
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# LEBANONUPRISING: a thorough study of Lebanese tweets

**Authors:** Reda Khalaf and Mireille Makary · **Year:** 2020 · **Venue:** arXiv:2009.14459 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2009.14459) · [PDF](https://arxiv.org/pdf/2009.14459) · [DOI 10.48550/arxiv.2009.14459](https://doi.org/10.48550/arxiv.2009.14459) · [arXiv 2009.14459](https://arxiv.org/abs/2009.14459) · [OpenAlex](https://openalex.org/W3090736700)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
The paper presents what the authors describe as the first sentiment study of tweets in spoken Lebanese Arabic, a dialect with no prior labelled resources. Its data comes from the LebanonUprising hashtag that trended during the October 2019 socio-economic protests. The authors filtered a public Kaggle collection down to Lebanese-dialect tweets and had them hand-labelled with a custom web tool. To normalise dialect spellings, they built a Lebanese–MSA mapping dictionary. Tweets were then cleaned, stripped of stopwords and stemmed. Five scikit-learn classifiers were trained for binary positive/negative prediction and applied to a second set of tweets collected months later. All classifiers performed at a very similar level. The authors also tested whether particular emojis can signal sarcastic or funny tweets and report satisfactory results. A user-overlap analysis showed that the hashtag's audience largely changed over time. The work matters as a starting resource for an under-served Arabic dialect, though its bag-of-words pipeline is basic.

## Key points
- **TDS (training set):** 21,529 Lebanese tweets, filtered from about 100K tweets collected by Abed Khooli on 18–21 October 2019 and shared on Kaggle. **PDS (prediction set):** 24,798 tweets collected from May to 8 August 2020 with workbench data.
- **Annotation:** five annotators used six labels (sarcastic, angry, negative, neutral, funny, positive). These were collapsed into a binary scheme with neutral tweets dropped, giving 8,943 negative and 12,586 positive tweets.
- **Pipeline:** Lebanese→MSA dictionary mapping, then removal of links, emojis, punctuation and stopwords, then the Snowball stemmer.
- **Classifiers:** MultinomialNB, SVM, KNN, SGDClassifier and Logistic Regression, tuned with grid search on an 85/15 split. Their accuracies were close to one another.
- **User overlap:** TDS had 38,322 users with retweets and 9,316 without. PDS had 20,850 and 5,406. Only 3,425 users appeared in both, about 5%.

## Interesting ideas
- Map dialect spelling variants onto one standard form before stemming, so that variant spellings of the same word count as one feature.
- Annotate with fine-grained labels, collapse them into a coarse task for training, and keep flags for the subtypes (sarcastic, funny) to test later hypotheses.
- Use the presence of specific emojis as a simple, transparent cue for sarcasm or humour.
- Train on one time period and predict on a later one to see how a model behaves under topic and audience drift.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (tokens): A hand-curated map from nonstandard or informal English spellings to canonical forms could run before the lemmatizer, and each rewrite could be logged so it stays explainable.
- **I2** (learn): Silver or teacher annotations could store detailed tags even when training targets a simpler scheme, so later experiments need no relabelling.
- **I3** (frames): The irony module could treat such surface markers as explicit, explainable evidence that feeds an irony or implicature decision rather than deciding it alone.
- **I4** (learn): Evaluation could include held-out text from a different period or source as a shift test alongside in-domain scores.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

Also on the byline: [Lebanese International University](https://ror.org/034agrd14) · [Lebanese International University](https://ror.org/027anng05).

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 9,086 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 8,889 works · [💭 Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/) · 798 works · [💭 Experimental and Cognitive Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/) · 463 works

🧭 Topics (1): [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 1,484 works

## Cite

Cite as: Reda Khalaf and Mireille Makary. 2020. LEBANONUPRISING: a thorough study of Lebanese tweets. arXiv:2009.14459.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{khalaf2020lebanonuprising,
  title = {{LEBANONUPRISING: a thorough study of Lebanese tweets}},
  author = {Reda Khalaf and Mireille Makary},
  year = {2020},
  doi = {10.48550/arxiv.2009.14459},
  eprint = {2009.14459},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2009.14459}
}
```

- [← previous Learning synchronous context-free grammars with multiple specialised non-terminals for hierarchical phrase-based translation](https://md.research.elamur.ai/free/en/papers/sanchez-martinez-2020-learning-synchronous-context-free-grammars/)
- [next → Linguistic Features in Automatic Sarcasm Detection](https://md.research.elamur.ai/free/en/papers/ducret-2020-linguistic-features-automatic-sarcasm-detection/)
- [↑ back to papers of 2020](https://md.research.elamur.ai/free/en/papers/2020/page/9/)

## References

16 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Kouloumpis, Efthymios., Wilson, T., Moore, J. (2011) Twitter sentiment analysis: the good the bad and the OMG! ICWSM 11, 538–541 [find](https://scholar.google.com/scholar?q=Kouloumpis%2C+Efthymios%2E%2C+Wilson%2C+T%2E%2C+Moore%2C+J%2E+%282011%29+Twitter+sentiment+analysis%3A+the+good+the+bad+and+the+OMG%21+ICWSM+11%2C+538%E2%80%93541)
2. Ibrahim, H.S., Abdou, S.M., Gheith, M. (2015) Sentiment analysis for modern standard Arabic and colloquial. arXiv preprint, arXiv:1505.03105 [↗](https://arxiv.org/abs/1505.03105)
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6. Parrott, W (2001). Emotions in social psychology: Essential readings. Psychology Press. Excluding retweets 9,316 5,406 [find](https://scholar.google.com/scholar?q=Parrott%2C+W+%282001%29%2E+Emotions+in+social+psychology%3A+Essential+readings%2E+Psychology+Press%2E+Excluding+retweets+9%2C316+5%2C406)
7. Korayem, M., Crandall, D., Abdul-Mageed, M (2012) Subjectivity and sentiment analysis of Arabic: a survey. In: Hassanien, A.E., Salem, A.-B.M., Ramadan, R., Kim, T. (eds.) AMLTA 2012. CCIS, vol. 322, pp. 128–139. Springer, Heidelberg. doi:10.1007/978-3-642-35326-0 14 [↗](https://doi.org/10.1007/978-3-642-35326-0)
8. Alwakid, Ghadah, Osman, Taha, Hughes-Roberts, Thomas (2017) Challenges in Sentiment Analysis for Arabic Social Networks 3rd International Conference on Arabic Computational Linguistics, ACLing 2017, Dubai, UAE [find](https://scholar.google.com/scholar?q=Alwakid%2C+Ghadah%2C+Osman%2C+Taha%2C+Hughes%2DRoberts%2C+Thomas+%282017%29+Challenges+in+Sentiment+Analysis+for+Arabic+Social+Networks+3rd+International+Conference+on+Arabic+Computational+Linguistics%2C+ACLing+2017%2C+D)
9. Duwairi, Rehab & Marji, Raed & Sha'ban, Narmeen & Rushaidat, Sally. (2014). Sentiment Analysis in Arabic tweets. 2014 5th International Conference on Information and Communication Systems, ICICS 2014. 1-6. 10.1109/IACS.2014.6841964. [↗](https://doi.org/10.1109/iacs.2014.6841964)
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11. Lewis, David. (1998) Naive Bayes at forty: the independence assumption in information retrieval. In Machine Learning: ECML-98, Proceedings of the 10th European Conference on Machine Learning, Chemnitz, Germany (pp. 4–15). Berlin: Springer. [find](https://scholar.google.com/scholar?q=Lewis%2C+David%2E+%281998%29+Naive+Bayes+at+forty%3A+the+independence+assumption+in+information+retrieval%2E+In+Machine+Learning%3A+ECML%2D98%2C+Proceedings+of+the+10th+European+Conference+on+Machine+Learning%2C+Chemnitz)
12. Fix, Evelyn, and J. L. Hodges. (1989) Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties. International Statistical Review / Revue Internationale De Statistique 57, no. 3: 238-47. Accessed August 26, 2020. doi:10.2307/1403797. [↗](https://doi.org/10.2307/1403797)
13. Abdullah, Malak & Hadzikadic, Mirsad. (2017). Sentiment Analysis on Arabic Tweets: Challenges to Dissecting the Language. 191-202. 10.1007/978-3-319-58562-8_15. [↗](https://doi.org/10.1007/978-3-319-58562-8_15)
14. Al-Twairesh, Nora, Hend Al-Khalifa, AbdulMalik Alsalman, and Yousef Al-Ohali. (2018) Sentiment analysis of arabic tweets: Feature engineering and a hybrid approach. arXiv preprint arXiv:1805.08533. [↗](https://arxiv.org/abs/1805.08533)
15. Ruder, Sebastian. (2016). An overview of gradient descent optimization algorithms. ArXiv Preprint ArXiv:1609.04747. [↗](https://arxiv.org/abs/1609.04747)
16. Subramanian, Jayashree & Sridharan, Varun & Shu, Kai & Liu, Huan. (2019). Exploiting Emojis for Sarcasm Detection. Authors Reda Khalaf: Master student in the Computer Science and Information Technology Department at the Lebanese International University Mireille Makary, PhD: Lecturer in the Computer Science and Information Technology Department at the Lebanese International University with research focus on machine learning and information retrieval. [find](https://scholar.google.com/scholar?q=Subramanian%2C+Jayashree+%26+Sridharan%2C+Varun+%26+Shu%2C+Kai+%26+Liu%2C+Huan%2E+%282019%29%2E+Exploiting+Emojis+for+Sarcasm+Detection%2E+Authors+Reda+Khalaf%3A+Master+student+in+the+Computer+Science+and+Information+Technolog)

*Updated 24 October 2026*