---
title: "Entity-Level Sentiment Analysis with — Rønningstad 2026"
description: "This paper addresses entity-level sentiment analysis (Elsa), where a model must assign a document-level polarity to each person or organization mentioned…"
authors: ["Egil Rønningstad", "Roman Klinger", "Lilja Øvrelid", "Erik Velldal"]
year: 2026
venue: "Proceedings of the Fifteenth Language Resources and Evaluation Conference"
source: "https://aclanthology.org/2026.lrec-1.638/"
doi: "10.63317/35ideqx4jk89"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/ronningstad-2026-entity-level-sentiment-analysis-sentence/"
---
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# Entity-Level Sentiment Analysis with Sentence Relevance Detection

**Authors:** [Egil Rønningstad](https://md.research.elamur.ai/free/en/authors/egil-ronningstad/), [Roman Klinger](https://md.research.elamur.ai/free/en/authors/roman-klinger/), [Lilja Øvrelid](https://md.research.elamur.ai/free/en/authors/lilja-ovrelid/), [Erik Velldal](https://md.research.elamur.ai/free/en/authors/erik-velldal/) · **Year:** 2026 · **Venue:** Proceedings of the Fifteenth Language Resources and Evaluation Conference · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.lrec-1.638/) · [PDF](https://aclanthology.org/2026.lrec-1.638.pdf) · [DOI 10.63317/35ideqx4jk89](https://doi.org/10.63317/35ideqx4jk89) · [OpenAlex](https://openalex.org/W7160045307)

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

## Summary
This paper addresses entity-level sentiment analysis (Elsa), where a model must assign a document-level polarity to each person or organization mentioned in a text. Most Norwegian Elsa reviews (drawn from NoReC) exceed the 512-token limit of encoders such as NorBert3-large. Rather than chunking documents and aggregating predictions, the authors train a model to keep only the sentences or spans relevant to a given entity. This compressed input lets encoders read the whole relevant context, and it also shortens training and inference for decoders.

For sub-10B decoder models, the paper compares prompting with two fine-tuning regimes, supervised fine-tuning (SFT) and pairwise comparison (PC). PC is borrowed from reward modeling: the model learns to prefer a text stating the correct label over one stating a wrong label.

Both fine-tuned decoders beat earlier Elsa baselines and fine-tuned encoders. PC is simpler, faster and more stable than SFT, and it also performs strongly on PerSenT and on EuroEval sentiment tasks in Norwegian and English. This makes PC a practical new approach to text classification.

## Key points
- **Datasets:** The Norwegian Elsa dataset has 2,479 sentiment-annotated entities, and about 60% of its training texts exceed 512 tokens. The English PerSenT dataset has about 25% of texts over that limit. Earlier Elsa baselines reached 68.1% weighted F1 (NorBert3-large, sentence aggregation) and 73.3% (zero-shot GPT-4).
- **PC setup:** With gemma-2-9b, building two training pairs per entity (one for each wrong label) raised weighted F1 from 80.15% to 81.72%. Two training epochs worked best, with eval accuracy of 0.875.
- **Prompt language and efficiency:** Norwegian prompts averaged 83.44% accuracy versus 82.20% for English prompts. The authors fine-tuned with LoRA (rank 16, alpha 32). 4-bit quantization cost little, giving 82.25% versus 82.87% at 16-bit precision.
- **Compression check:** In a manual review of 50 non-neutral entities, the sentiment signal stayed intact in 43 cases while texts shrank to 40–64% of their original length. The ent2ent heuristic lost the signal twice, both times when the sentiment appeared before the entity was first named.
- **Code:** Available at github.com/egilron/elsa-lrec.

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

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/)

## Cite

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

```bibtex
@inproceedings{ronningstad2026entity,
  title = {{Entity-Level Sentiment Analysis with Sentence Relevance Detection}},
  author = {Rønningstad, Egil and Klinger, Roman and Øvrelid, Lilja and Velldal, Erik},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/35ideqx4jk89},
  url = {https://doi.org/10.63317/35ideqx4jk89}
}
```

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