@inproceedings{bastan-etal-2020-authors,
title = "Author{'}s Sentiment Prediction",
author = "Bastan, Mohaddeseh and
Koupaee, Mahnaz and
Son, Youngseo and
Sicoli, Richard and
Balasubramanian, Niranjan",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.52",
doi = "10.18653/v1/2020.coling-main.52",
pages = "604--615",
abstract = "Even though sentiment analysis has been well-studied on a wide range of domains, there hasn{'}tbeen much work on inferring author sentiment in news articles. To address this gap, we introducePerSenT, a crowd-sourced dataset that captures the sentiment of an author towards the mainentity in a news article. Our benchmarks of multiple strong baselines show that this is a difficultclassification task. BERT performs the best amongst the baselines. However, it only achievesa modest performance overall suggesting that fine-tuning document-level representations aloneisn{'}t adequate for this task. Making paragraph-level decisions and aggregating over the entiredocument is also ineffective. We present empirical and qualitative analyses that illustrate thespecific challenges posed by this dataset. We release this dataset with 5.3k documents and 38kparagraphs with 3.2k unique entities as a challenge in entity sentiment analysis.",
}
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<abstract>Even though sentiment analysis has been well-studied on a wide range of domains, there hasn’tbeen much work on inferring author sentiment in news articles. To address this gap, we introducePerSenT, a crowd-sourced dataset that captures the sentiment of an author towards the mainentity in a news article. Our benchmarks of multiple strong baselines show that this is a difficultclassification task. BERT performs the best amongst the baselines. However, it only achievesa modest performance overall suggesting that fine-tuning document-level representations aloneisn’t adequate for this task. Making paragraph-level decisions and aggregating over the entiredocument is also ineffective. We present empirical and qualitative analyses that illustrate thespecific challenges posed by this dataset. We release this dataset with 5.3k documents and 38kparagraphs with 3.2k unique entities as a challenge in entity sentiment analysis.</abstract>
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%0 Conference Proceedings
%T Author’s Sentiment Prediction
%A Bastan, Mohaddeseh
%A Koupaee, Mahnaz
%A Son, Youngseo
%A Sicoli, Richard
%A Balasubramanian, Niranjan
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F bastan-etal-2020-authors
%X Even though sentiment analysis has been well-studied on a wide range of domains, there hasn’tbeen much work on inferring author sentiment in news articles. To address this gap, we introducePerSenT, a crowd-sourced dataset that captures the sentiment of an author towards the mainentity in a news article. Our benchmarks of multiple strong baselines show that this is a difficultclassification task. BERT performs the best amongst the baselines. However, it only achievesa modest performance overall suggesting that fine-tuning document-level representations aloneisn’t adequate for this task. Making paragraph-level decisions and aggregating over the entiredocument is also ineffective. We present empirical and qualitative analyses that illustrate thespecific challenges posed by this dataset. We release this dataset with 5.3k documents and 38kparagraphs with 3.2k unique entities as a challenge in entity sentiment analysis.
%R 10.18653/v1/2020.coling-main.52
%U https://aclanthology.org/2020.coling-main.52
%U https://doi.org/10.18653/v1/2020.coling-main.52
%P 604-615
Markdown (Informal)
[Author’s Sentiment Prediction](https://aclanthology.org/2020.coling-main.52) (Bastan et al., COLING 2020)
ACL
- Mohaddeseh Bastan, Mahnaz Koupaee, Youngseo Son, Richard Sicoli, and Niranjan Balasubramanian. 2020. Author’s Sentiment Prediction. In Proceedings of the 28th International Conference on Computational Linguistics, pages 604–615, Barcelona, Spain (Online). International Committee on Computational Linguistics.