# Author's Sentiment Prediction

arXiv: https://arxiv.org/abs/2011.06128 · HTML: https://mlsanity.com/paper/2011.06128/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2020.

## Built on by (most cited first, 18 of 18)

- [Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks](https://mlsanity.com/paper/2204.07705/) (2022)
- [Analyzing Dataset Annotation Quality Management in the Wild](https://mlsanity.com/paper/2307.08153/) (2023)
- [TellMeWhy: A Dataset for Answering Why-Questions in Narratives](https://mlsanity.com/paper/2106.06132/) (2021)
- [Prompting with Pseudo-Code Instructions](https://mlsanity.com/paper/2305.11790/) (2023)
- [CaT-BENCH: Benchmarking Language Model Understanding of Causal and Temporal Dependencies in Plans](https://mlsanity.com/paper/2406.15823/) (2024)
- [Massively Multilingual Corpus of Sentiment Datasets and Multi-faceted Sentiment Classification Benchmark](https://mlsanity.com/paper/2306.07902/) (2023)
- [Benchmarking LLMs for Political Science: A United Nations Perspective](https://mlsanity.com/paper/2502.14122/) (2025)
- [Deciphering Political Entity Sentiment in News with Large Language Models: Zero-Shot and Few-Shot Strategies](https://mlsanity.com/paper/2404.04361/) (2024)
- [A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2204.05356/) (2022)
- [From Big to Small Without Losing It All: Text Augmentation with ChatGPT for Efficient Sentiment Analysis](https://mlsanity.com/paper/2312.04720/) (2023)
- [Entity Framing and Role Portrayal in the News](https://mlsanity.com/paper/2502.14718/) (2025)
- [POQue: Asking Participant-specific Outcome Questions for a Deeper Understanding of Complex Events](https://mlsanity.com/paper/2212.02629/) (2022)
- [On Positivity Bias in Negative Reviews](https://mlsanity.com/paper/2106.12056/) (2021)
- [Assessment of Massively Multilingual Sentiment Classifiers](https://mlsanity.com/paper/2204.04937/) (2022)
- [SuMe: A Dataset Towards Summarizing Biomedical Mechanisms](https://mlsanity.com/paper/2205.04652/) (2022)
- [Entity-Level Sentiment: More than the Sum of Its Parts](https://mlsanity.com/paper/2407.03916/) (2024)
- [Incentivizing High-Quality Human Annotations with Golden Questions](https://mlsanity.com/paper/2505.19134/) (2025)
- [Users as Annotators: LLM Preference Learning from Comparison Mode](https://mlsanity.com/paper/2510.13830/) (2025)

## Abstract (arXiv)

We introduce PerSenT, a dataset of crowd-sourced annotations of the sentiment expressed by the authors towards the main entities in news articles. The dataset also includes paragraph-level sentiment annotations to provide more fine-grained supervision for the task. Our benchmarks of multiple strong baselines show that this is a difficult classification task. The results also suggest that simply fine-tuning document-level representations from BERT isn't adequate for this task. Making paragraph-level decisions and aggregating them over the entire document is also ineffective. We present empirical and qualitative analyses that illustrate the specific challenges posed by this dataset. We release this dataset with 5.3k documents and 38k paragraphs covering 3.2k unique entities as a challenge in entity sentiment analysis.