Author’s Sentiment Prediction

Mohaddeseh Bastan, Mahnaz Koupaee, Youngseo Son, Richard Sicoli, Niranjan Balasubramanian


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.
Anthology ID:
2020.coling-main.52
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
604–615
Language:
URL:
https://aclanthology.org/2020.coling-main.52
DOI:
10.18653/v1/2020.coling-main.52
Bibkey:
Cite (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.
Cite (Informal):
Author’s Sentiment Prediction (Bastan et al., COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.52.pdf
Code
 StonyBrookNLP/PerSenT
Data
PerSenT