Author’s Sentiment Prediction
By Mohaddeseh Bastan, Mahnaz Koupaee, Youngseo Son, Richard Sicoli, Niranjan Balasubramanian. 2020
28 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Sentiment & Opinion Analysis
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In Artificial Intelligence
- Hannaneh Hajishirzi
- Chitta Baral
- Yejin Choi
- Swaroop Mishra
- Daniel Khashabi
- Noah A. Smith
- Luke S. Zettlemoyer
- Mohit Bansal
In Sentiment & Opinion Analysis
Citations in the corpus
- Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks (2022, methodology): “Table 15: An example task in the Title Generation category of our dataset, adopted from PerSenT (Bastan et al., 2020).”