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    <abstract>Fine-grained sentiment analysis attempts to extract sentiment holders, targets and polar expressions and resolve the relationship between them, but progress has been hampered by the difficulty of annotation. Targeted sentiment analysis, on the other hand, is a more narrow task, focusing on extracting sentiment targets and classifying their polarity. In this paper, we explore whether incorporating holder and expression information can improve target extraction and classification and perform experiments on eight English datasets. We conclude that jointly predicting target and polarity BIO labels improves target extraction, and that augmenting the input text with gold expressions generally improves targeted polarity classification. This highlights the potential importance of annotating expressions for fine-grained sentiment datasets. At the same time, our results show that performance of current models for predicting polar expressions is poor, hampering the benefit of this information in practice.</abstract>
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%0 Conference Proceedings
%T If you’ve got it, flaunt it: Making the most of fine-grained sentiment annotations
%A Barnes, Jeremy
%A Øvrelid, Lilja
%A Velldal, Erik
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F barnes-etal-2021-youve
%X Fine-grained sentiment analysis attempts to extract sentiment holders, targets and polar expressions and resolve the relationship between them, but progress has been hampered by the difficulty of annotation. Targeted sentiment analysis, on the other hand, is a more narrow task, focusing on extracting sentiment targets and classifying their polarity. In this paper, we explore whether incorporating holder and expression information can improve target extraction and classification and perform experiments on eight English datasets. We conclude that jointly predicting target and polarity BIO labels improves target extraction, and that augmenting the input text with gold expressions generally improves targeted polarity classification. This highlights the potential importance of annotating expressions for fine-grained sentiment datasets. At the same time, our results show that performance of current models for predicting polar expressions is poor, hampering the benefit of this information in practice.
%R 10.18653/v1/2021.eacl-main.5
%U https://aclanthology.org/2021.eacl-main.5
%U https://doi.org/10.18653/v1/2021.eacl-main.5
%P 49-62

Markdown (Informal)

[If you’ve got it, flaunt it: Making the most of fine-grained sentiment annotations](https://aclanthology.org/2021.eacl-main.5) (Barnes et al., EACL 2021)

• If you’ve got it, flaunt it: Making the most of fine-grained sentiment annotations (Barnes et al., EACL 2021)

ACL

• Jeremy Barnes, Lilja Øvrelid, and Erik Velldal. 2021. If you’ve got it, flaunt it: Making the most of fine-grained sentiment annotations. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 49–62, Online. Association for Computational Linguistics.