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# HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition

**Authors:** Avihay Chriqui; Inbal Yahav · **Year:** 2021 · **Venue:** arXiv:2102.01909 (v3) **Link:** [arxiv.org/abs/2102.01909](https://arxiv.org/abs/2102.01909) · library `langstream-2021-chriqui-hebert-hebemo-hebrew-bert-model` · **Licence:** [arXiv — non-exclusive license to distribute](https://arxiv.org/licenses/nonexclusive-distrib/1.0/license.html) — summary only

[**44** citations](https://openalex.org/works?filter=cites:W4281673713) openalex · 2026-10-18[**24** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/langstream-2021-chriqui-hebert-hebemo-hebrew-bert-model/) of the 140673 papers whose bibliographies we read[arXiv:2102.01909](https://openalex.org/S4210202349)[licence](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

- [Publisher's page](https://doi.org/10.1287/ijds.2022.0016)
- [PDF](https://arxiv.org/pdf/2102.01909)
- [DOI 10.1287/ijds.2022.0016](https://doi.org/10.1287/ijds.2022.0016)
- [arXiv 2102.01909](https://arxiv.org/abs/2102.01909)
- [OpenAlex](https://openalex.org/W4281673713)

**Authors (2)**

1. [Avihay Chriqui](https://openalex.org/A5065155013) [ORCID](https://orcid.org/0000-0003-2498-377X) · [Semantic Scholar](https://www.semanticscholar.org/author/2047715465) · h 2 · 46 citations · 4 works
2. [Inbal Yahav](https://openalex.org/A5070441489) [ORCID](https://orcid.org/0000-0002-1513-017X) · [Semantic Scholar](https://www.semanticscholar.org/author/2487267) · h 15 · 994 citations · 84 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

The authors pre-train HeBERT, a BERT model for modern Hebrew, on Hebrew Wikipedia and OSCAR, and test it on fill-in-the-blank, out-of-vocabulary, NER, POS and polarity tasks. They find that larger models do better on tasks about single terms, while a smaller model captures the sentiment of a whole sentence better. HebEMO is a document-level tool built on HeBERT for polarity and emotion. It is trained on Israeli news comments about Covid-19 from 2020, labelled with an active-learning loop. It reaches weighted F1 0.96 for polarity and about 0.8 to 0.97 for most emotions, but only 0.41 for surprise.

## Ideas

- I1. A per-category test: the surprise result shows that one average hides a category the model cannot find. Mova's emotion and attitude rules in `prag` should be scored per category, and the weakest category tested first.
- I2. Match model size to the task: the paper's split between term-level and sentence-level tasks is an argument for small, measured rules over one large model. Each Mova module should be judged on its own task.
- I3. Build a Hebrew emotion lexicon test from the paper's category list (anger, fear, joy, surprise and the others), using the labelled comments if their licence allows, and otherwise a small set Mova labels itself.

## For Mova

I1 and I3 apply to the Hebrew pragmatics plan in [9. Pragmatics and politeness](https://md.mova.elamur.ai/free/en/docs/languages/he/09-pragmatics-politeness/) and to the rules in `prag`. I2 is a design rule for [12. Mova and Modern Hebrew: where it stands, what it would need](https://md.mova.elamur.ai/free/en/docs/languages/he/12-mova/). The model itself is neural, so only the task lists and measures are reused.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.1287/ijds.2022.0016](https://doi.org/10.1287/ijds.2022.0016) · [arXiv 2102.01909](https://arxiv.org/abs/2102.01909)

```
@misc{chriqui2021hebert,
  title = {{HeBERT \& HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition}},
  author = {Chriqui, Avihay and Yahav, Inbal},
  year = {2021},
  doi = {10.1287/ijds.2022.0016},
  eprint = {2102.01909},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1287/ijds.2022.0016}
}
```

- [← previous An Arabic-Hebrew parallel corpus of TED talks](https://md.mova.elamur.ai/free/en/docs/papers/langstream-2016-cettolo-arabic-hebrew-parallel-corpus-ted/)
- [next → AlephBERT: A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application With](https://md.mova.elamur.ai/free/en/docs/papers/langstream-2021-seker-alephbert-hebrew-large-pre-trained/)
- [↑ back to Modern Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/he/)

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