---
title: "Arabic Emoji Sentiment Lexicon (Arab-ESL): A Comparison"
description: "The authors test whether this holds for sentiment by comparing Arabic and European… 0.275. kappa. 59 references, cited by 3 papers here."
authors: ["Shatha Ali A. Hakami", "Robert Hendley", "Phillip Smith"]
year: 2021
venue: "Proceedings of the Sixth Arabic Natural Language Processing Workshop"
source: "https://aclanthology.org/2021.wanlp-1.7/"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/hakami-2021-arabic-emoji-sentiment-lexicon-arab/"
---
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# Arabic Emoji Sentiment Lexicon (Arab-ESL): A Comparison between Arabic and European Emoji Sentiment Lexicons

**Authors:** [Shatha Ali A. Hakami](https://md.research.elamur.ai/free/en/authors/shatha-ali-a-hakami/), [Robert Hendley](https://md.research.elamur.ai/free/en/authors/robert-hendley/) and [Phillip Smith](https://md.research.elamur.ai/free/en/authors/phillip-smith/) · **Year:** 2021 · **Venue:** [Proceedings of the Sixth Arabic Natural Language Processing Workshop](https://md.research.elamur.ai/free/en/venues/wanlp/) ([WANLP 2021](https://md.research.elamur.ai/free/en/venues/wanlp/2021/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2021.wanlp-1.7/) · [PDF](https://aclanthology.org/2021.wanlp-1.7.pdf) · [OpenAlex](https://openalex.org/W3155658398)

**Citations:** 15 citations (OpenAlex, 2026-10-14) · [3](https://md.research.elamur.ai/free/en/papers/hakami-2021-arabic-emoji-sentiment-lexicon-arab/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Emoji are often assumed to carry the same meaning everywhere. The authors test whether this holds for sentiment by comparing Arabic and European usage. Existing emoji sentiment resources were almost entirely non-Arabic, so they first built Arab-ESL. It is an Arabic emoji sentiment lexicon of 1034 emoji, derived from 144,196 tweets taken from existing Arabic datasets that together cover the Arabic dialects. They then lined it up against the Emoji Sentiment Ranking of Kralj Novak et al., which they call Euro-ESL. The comparison is done per emoji category with two measures: Pearson correlation of the continuous sentiment scores, and Cohen's Kappa on the negative/neutral/positive labels.

The two lexicons agree well for hearts, facial expressions and body language. This fits the idea that basic emotions are universal. Agreement is weak or absent for food, symbols and human activities, and food diverges the most. Some items even flip polarity between the two cultures. The main contribution is an open Arabic resource, together with evidence that emoji sentiment lexicons do not transfer safely across cultures.

## Key points
- **Arab-ESL:** 1034 emoji, built from 144,196 Arabic tweets drawn from existing datasets. It is released openly on GitHub as Arabic-Emoji-Sentiment-Lexicon-Version-1.0.
- **Euro-ESL** (Emoji Sentiment Ranking) is the reference lexicon:
  - 751 emoji from more than 70,000 tweets in 13 European languages, labelled by 83 native-speaker annotators.
  - Each emoji's score comes from the sentiment of the tweets that contain it.
- **Highest Pearson correlations:** Heart 0.834 and Facial Expression 0.747. Label agreement for Heart was almost perfect and for Facial Expression moderate.
- **Lowest correlations:** Food −0.159, Symbol 0.275, Human Activities 0.275. Kappa values were near zero or negative: Food 0.0160, Symbol 0.0581, Human Activity −0.0556.
- **Polarity flips among food emoji:**
  - Fish Cake with Swirl is positive in Arab-ESL (0.576) but negative in Euro-ESL (−0.594).
  - Beer Mug is negative in Arab-ESL (−0.207) but positive in Euro-ESL (0.493).

## Interesting ideas
- Score a symbol's polarity from the sentiment of the messages it appears in, instead of asking annotators to rate the symbol on its own; this captures how the symbol is actually used.
- Compare two resources twice, once with correlation on continuous scores and once with Kappa on the discretized labels, because the two checks can reveal different kinds of mismatch.
- Break agreement down by semantic category rather than reporting one global number, which shows where a resource transfers (emotions) and where it breaks (food, symbols, activities).
- Treat polarity attached to culturally loaded items (food, drink, symbols) as context-dependent, since the same item can be positive in one community and negative in another.
- The related work cited here sorts emoji into true, multi-sentiment, ambiguous, or no-sentiment indicators rather than giving each one a single score.

## For Mova

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

- **I1** (world): evaluative values for words or phrases in the compiled knowledge could be estimated from sentence-level judgements in our corpora, with the source sentences kept as explainable evidence.
- **I2** (learn): when comparing lexicon versions or witness outputs, report a graded agreement and a categorical agreement side by side so threshold artefacts are not mistaken for real disagreement.
- **I3** (learn): evaluations of silver data or transferred knowledge could be split by WordNet supersense or verb class to locate the domains where witnesses systematically disagree.
- **I4** (frames): implicature and irony detection should mark such evaluations as culture-sensitive, so the default reading can be overridden instead of hard-coding one polarity.
- **I5** (frames): emoji and similar cues could carry a role tag of this kind, so that an ambiguous or neutral cue does not push a speech-act or attitude decision.

*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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## Cite

Cite as: Shatha Ali A. Hakami, Robert Hendley and Phillip Smith. 2021. Arabic Emoji Sentiment Lexicon (Arab-ESL): A Comparison between Arabic and European Emoji Sentiment Lexicons. Proceedings of the Sixth Arabic Natural Language Processing Workshop.

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

```bibtex
@inproceedings{hakami2021arabic,
  title = {{Arabic Emoji Sentiment Lexicon (Arab-ESL): A Comparison between Arabic and European Emoji Sentiment Lexicons}},
  author = {Shatha Ali A. Hakami, Robert Hendley and Phillip Smith},
  booktitle = {Proceedings of the Sixth Arabic Natural Language Processing Workshop},
  year = {2021},
  url = {https://aclanthology.org/2021.wanlp-1.7/}
}
```

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*Updated 2 November 2026*