---
title: "LABR: A Large Scale Arabic Sentiment Analysis Benchmark (2014)"
description: "They describe the corpus statistics and define two tasks: positive/negative polarity and full star-rating… 30 references, cited by 11 papers here."
authors: ["Mahmoud Nabil", "Mohamed Aly", "Amir Atiya"]
year: 2014
venue: "arXiv:1411.6718"
source: "https://arxiv.org/abs/1411.6718"
doi: "10.48550/arxiv.1411.6718"
arxiv: "1411.6718"
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/nabil-2014-labr-large-scale-arabic-sentiment/"
---
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# LABR: A Large Scale Arabic Sentiment Analysis Benchmark

**Authors:** [Mahmoud Nabil](https://md.research.elamur.ai/free/en/authors/mahmoud-nabil/), [Mohamed Aly](https://md.research.elamur.ai/free/en/authors/mohamed-aly/) and [Amir Atiya](https://md.research.elamur.ai/free/en/authors/amir-atiya/) · **Year:** 2014 · **Venue:** arXiv:1411.6718 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1411.6718) · [PDF](https://arxiv.org/pdf/1411.6718) · [DOI 10.48550/arxiv.1411.6718](https://doi.org/10.48550/arxiv.1411.6718) · [arXiv 1411.6718](https://arxiv.org/abs/1411.6718) · [OpenAlex](https://openalex.org/W1552146667)

**Citations:** 30 citations (OpenAlex, 2026-10-14) · [11](https://md.research.elamur.ai/free/en/papers/nabil-2014-labr-large-scale-arabic-sentiment/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Arabic sentiment analysis has lacked large, open, comparable data. Earlier Arabic corpora held a few thousand examples at most, were mostly not released, and had no fixed train/test division. The authors built LABR by harvesting book reviews from Goodreads, cleaning them and keeping only purely Arabic texts. The result is 63,257 reviews, each carrying a 1–5 star rating. They describe the corpus statistics and define two tasks: positive/negative polarity and full star-rating prediction. For both tasks they publish fixed training, validation and test splits, in balanced and unbalanced variants. They then run a broad set of standard text classifiers as baselines; SVM and logistic regression come out strongest. They also derive a domain-specific sentiment lexicon from the training portion, containing single words and multi-word expressions. That lexicon reaches competitive accuracy while using only a small share of the features. The dataset and reproduction scripts are public, so LABR can serve as a common benchmark for Arabic opinion mining.

## Key points
- Collection: over 220,000 reviews were crawled from goodreads.com in March 2013, taken from the first 2,143 titles of a "Best Arabic Books" list. More than 70% turned out to be non-Arabic and were discarded.
- Cleaning: the authors stripped HTML, links, newlines and decorative Unicode, and collapsed repeated dots. Any review containing characters outside Arabic letters, digits and punctuation was then dropped.
- Final corpus statistics:
  - 63,257 reviews, 16,486 users and 2,131 books.
  - About 4.13M tokens and 342,199 sentences.
  - Median review length is 33 tokens, the mean is 65 and the maximum is 3,736.
- The ratings are heavily skewed toward 4–5 stars. The labels are also noisy, and 3-star reviews are ambiguous in polarity.
- Comparison with earlier Arabic sets: OCA, AWATIF, TAGREED, TAHRIR and MONTADA range from 500 to about 3,100 items, so LABR is roughly an order of magnitude larger.

## Interesting ideas
- Releasing fixed splits for each task, in both balanced and natural-skew versions, separates "can the model discriminate" from "does it survive real class priors."
- A sentiment lexicon mined from training data can match a full feature set while using far fewer features. This makes the lexicon a compact, inspectable stand-in for opaque models.
- The lexicon includes compound expressions, because a phrase's sentiment often differs from that of its parts.
- The authors note that star ratings and review text often disagree, as with sarcasm or positive scores attached to complaints. Such cases are a natural pool of irony or noise examples.
- A strict character-whitelist filter is a cheap, transparent way to keep a crawled corpus monolingual before any modelling.

## For Mova

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

- **I1** (learn): Mova's evaluation sets could ship in a skewed and a balanced form. This would show whether a reader's gains come from real understanding or from leaning on majority classes.
- **I2** (world): polarity values for words and phrases could be compiled from a corpus into the knowledge layer. Each value would carry provenance, so evaluative states attached to entities stay explainable.
- **I3** (frames): evaluative meaning should be computed over multiword units or subtrees, not summed word by word. This fits Mova's no-bag-of-words principle and helps implicature handling.
- **I4** (frames): paired signals like rating versus text could flag candidate ironic utterances for the irony detector. They could also serve as negative controls when testing it.
- **I5** (tokens): a similar script whitelist could gate silver input text, keeping mixed-script noise away from the tokenizer and tagger.

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

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🧭 Topics (3): [😊 Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · 972 works · [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 2,908 works · [📏 Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · 1,220 works

## Cite

Cite as: Mahmoud Nabil, Mohamed Aly and Amir Atiya. 2014. LABR: A Large Scale Arabic Sentiment Analysis Benchmark. arXiv:1411.6718.

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

```bibtex
@misc{nabil2014labr,
  title = {{LABR: A Large Scale Arabic Sentiment Analysis Benchmark}},
  author = {Mahmoud Nabil, Mohamed Aly and Amir Atiya},
  year = {2014},
  doi = {10.48550/arxiv.1411.6718},
  eprint = {1411.6718},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1411.6718}
}
```

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## References

30 references, 7 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 1 November 2026*