@inproceedings{ronningstad-etal-2026-entity,
title = "Entity-Level Sentiment Analysis with Sentence Relevance Detection",
author = "R{\o}nningstad, Egil and
Klinger, Roman and
{\O}vrelid, Lilja and
Velldal, Erik",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.638/",
doi = "10.63317/35ideqx4jk89",
pages = "8040--8055",
abstract = "The task of entity-level sentiment analysis (Elsa) is to extract sentiment scores for a given entity (such as person names or organization names) from a text. Elsa is a challenging task and involves processing of longer documents, where several entities may be mentioned with varying importance for the final score aggregation. Fine-tuning encoder-based Transformers (such as BERT) constitutes the state of the art for sentiment predictions, however, these models are still limited by their restricted input lengths. Decoder-only models so far still underperform on the task. We approach the context limitation by learning to extract segments that are relevant for the sentiment prediction for a given entity, without preprocessing by chunking and aggregation. For decoder models, we explore fine-tuning these through supervised fine-tuning and pairwise comparison, a method borrowed from reward modeling for preference optimization. Both methods perform well and set a new standard for the Elsa task. We further show that pairwise classification is faster, simpler, and shows less variance than the more common direct supervision for this task."
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<abstract>The task of entity-level sentiment analysis (Elsa) is to extract sentiment scores for a given entity (such as person names or organization names) from a text. Elsa is a challenging task and involves processing of longer documents, where several entities may be mentioned with varying importance for the final score aggregation. Fine-tuning encoder-based Transformers (such as BERT) constitutes the state of the art for sentiment predictions, however, these models are still limited by their restricted input lengths. Decoder-only models so far still underperform on the task. We approach the context limitation by learning to extract segments that are relevant for the sentiment prediction for a given entity, without preprocessing by chunking and aggregation. For decoder models, we explore fine-tuning these through supervised fine-tuning and pairwise comparison, a method borrowed from reward modeling for preference optimization. Both methods perform well and set a new standard for the Elsa task. We further show that pairwise classification is faster, simpler, and shows less variance than the more common direct supervision for this task.</abstract>
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%0 Conference Proceedings
%T Entity-Level Sentiment Analysis with Sentence Relevance Detection
%A Rønningstad, Egil
%A Klinger, Roman
%A Øvrelid, Lilja
%A Velldal, Erik
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F ronningstad-etal-2026-entity
%X The task of entity-level sentiment analysis (Elsa) is to extract sentiment scores for a given entity (such as person names or organization names) from a text. Elsa is a challenging task and involves processing of longer documents, where several entities may be mentioned with varying importance for the final score aggregation. Fine-tuning encoder-based Transformers (such as BERT) constitutes the state of the art for sentiment predictions, however, these models are still limited by their restricted input lengths. Decoder-only models so far still underperform on the task. We approach the context limitation by learning to extract segments that are relevant for the sentiment prediction for a given entity, without preprocessing by chunking and aggregation. For decoder models, we explore fine-tuning these through supervised fine-tuning and pairwise comparison, a method borrowed from reward modeling for preference optimization. Both methods perform well and set a new standard for the Elsa task. We further show that pairwise classification is faster, simpler, and shows less variance than the more common direct supervision for this task.
%R 10.63317/35ideqx4jk89
%U https://aclanthology.org/2026.lrec-1.638/
%U https://doi.org/10.63317/35ideqx4jk89
%P 8040-8055
Markdown (Informal)
[Entity-Level Sentiment Analysis with Sentence Relevance Detection](https://aclanthology.org/2026.lrec-1.638/) (Rønningstad et al., LREC 2026)
ACL