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# Representations and Architectures in Neural Sentiment Analysis for Morphologically Rich Languages: A Case Study from Modern Hebrew

**Authors:** Adam Amram; Anat Ben David; [Reut Tsarfaty](https://md.mova.elamur.ai/free/en/docs/papers/authors/reut-tsarfaty/) · **Year:** 2018 · **Venue:** Proceedings of the 27th International Conference on Computational Linguistics, pp. 2242–2252 **Link:** [aclanthology.org/C18-1190](https://aclanthology.org/C18-1190/) · library `coling-2018-amram-representations-architectures-neural-sentiment-analysis` · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**19** citations](https://openalex.org/works?filter=cites:W2880029892) openalex · 2026-10-19[**24** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/coling-2018-amram-representations-architectures-neural-sentiment-analysis/) of the 140673 papers whose bibliographies we read[Proceedings of the 27th International Conference on Computational Linguistics](https://aclanthology.org/volumes/C18-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/C18-1190/)
- [PDF](https://aclanthology.org/C18-1190.pdf)
- [OpenAlex](https://openalex.org/W2880029892)

**Authors (3)**

1. [Adam Amram](https://openalex.org/A5029822097) [ORCID](https://orcid.org/0000-0003-2997-256X) · [Semantic Scholar](https://www.semanticscholar.org/author/35509647) · h 4 · 102 citations · 5 works
2. [Anat Ben David](https://openalex.org/A5058124570) [ORCID](https://orcid.org/0000-0003-4510-5634) · [Semantic Scholar](https://www.semanticscholar.org/author/1399062317) · h 14 · 830 citations · 60 works
3. [Reut Tsarfaty](https://openalex.org/A5063283689) [ORCID](https://orcid.org/0000-0001-6406-6568) · [Semantic Scholar](https://www.semanticscholar.org/author/2799181) · h 24 · 2886 citations · 217 works

Citations and author metrics from openalex (2026-10-19); authors with the same name are not always told apart.

## What it is about

The paper studies how two input choices change neural sentiment analysis for Modern Hebrew: token-based or morpheme-based input (morphemes from the YAP parser), and string-based or character-based vocabulary. It introduces a 12,804-comment Hebrew sentiment set from political Facebook pages (8,512 positive, 3,922 negative, 370 neutral; Cohen's kappa 0.697 between two coders). Five architectures are compared: linear, MLP, CNN, LSTM and BiLSTM. With string-based vocabulary, token-based input is best for the linear, MLP and CNN models (CNN 89.2 vs 89.1); the recurrent models lean the other way (LSTM 85.2 token vs 86.2 morpheme; BiLSTM 87.4 vs 87.5). The best model is a token-based CNN at about 89.2% accuracy, against a 66.6% majority baseline. Half of the errors on a 50-comment sample come from cases where the human coders also disagreed, mostly mixed sentiment or sarcasm. Errors in each architecture follow its own weakness: negation and third-party reference for the MLP, "sorry, but" for the CNN, length for the LSTM.

## Ideas

- I1. Report the majority-class baseline (66.6% here) beside every accuracy, and report the human kappa as the ceiling. Both are cheap and make any Hebrew sentiment or polarity figure readable.
- I2. A rule for honorific address to a public figure: a comment that opens with a title and then negates is a known false-negative pattern. Write it as a tagged pattern in [9. Pragmatics and politeness](https://md.mova.elamur.ai/free/en/docs/languages/he/09-pragmatics-politeness/) and count how often it fires on a held-out set.
- I3. Keep the data outside the repository. The released set is a candidate held-out test for polarity in `prag` only after its licence and the comment authors' privacy are checked; it is not training material for the release.

## For Mova

I1 goes into the measurement scripts for any Hebrew polarity test, in [8. Lexicon and register](https://md.mova.elamur.ai/free/en/docs/languages/he/08-lexicon-register/) where the register is discussed. I2 goes into [9. Pragmatics and politeness](https://md.mova.elamur.ai/free/en/docs/languages/he/09-pragmatics-politeness/). I3 is a note for `prag` and for the held-out set rules in `docs/`. The neural models are not used by Mova.

## Cite

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

```
@inproceedings{amram2018representations,
  title = {{Representations and Architectures in Neural Sentiment Analysis for Morphologically Rich Languages: A Case Study from Modern Hebrew}},
  author = {Amram, Adam and David, Anat Ben and Tsarfaty, Reut},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics},
  year = {2018},
  url = {https://aclanthology.org/C18-1190/}
}
```

- [← previous Word Segmentation, Unknown-word Resolution, and Morphological Agreement in a Hebrew Parsing System](https://md.mova.elamur.ai/free/en/docs/papers/cl-2013-goldberg-word-segmentation-unknown-word-resolution/)
- [next → IsraParlTweet: The Israeli Parliamentary and Twitter Resource](https://md.mova.elamur.ai/free/en/docs/papers/coling-2024-lan-israparltweet-israeli-parliamentary-twitter-resource/)
- [↑ back to Modern Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/he/)

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