# SEntFiN 1.0: Entity-Aware Sentiment Analysis for Financial News

arXiv: https://arxiv.org/abs/2305.12257 · HTML: https://mlsanity.com/paper/2305.12257/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 86 references that are in the map.
- First posted on arXiv 20 May 2023.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts](https://mlsanity.com/paper/1307.5336/) (2013)
- [BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1904.02232/) (2019)
- [Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence](https://mlsanity.com/paper/1903.09588/) (2019)
- [Impact of News on the Commodity Market: Dataset and Results](https://mlsanity.com/paper/2009.04202/) (2020)
- [Sentiment Analysis in the News](https://mlsanity.com/paper/1309.6202/) (2013)
- [Fortia-FBK at SemEval-2017 Task 5: Bullish or Bearish? Inferring Sentiment towards Brands from Financial News Headlines](https://mlsanity.com/paper/1704.00939/) (2017)
- [Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter: predicting sentiment from financial news headlines](https://mlsanity.com/paper/1705.00571/) (2017)

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

- [Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis](https://mlsanity.com/paper/2406.08627/) (2024)
- [Designing Heterogeneous LLM Agents for Financial Sentiment Analysis](https://mlsanity.com/paper/2401.05799/) (2024)
- [FinEntity: Entity-level Sentiment Classification for Financial Texts](https://mlsanity.com/paper/2310.12406/) (2023)
- [Both Matter: Enhancing the Emotional Intelligence of Large Language Models without Compromising the General Intelligence](https://mlsanity.com/paper/2402.10073/) (2024)
- [EFSA: Towards Event-Level Financial Sentiment Analysis](https://mlsanity.com/paper/2404.08681/) (2024)
- [CFBenchmark-MM: Chinese Financial Assistant Benchmark for Multimodal Large Language Model](https://mlsanity.com/paper/2506.13055/) (2025)
- [Are Large Language Models Reliable Reviewers? A Benchmark for Error Detection in Financial Documents](https://mlsanity.com/paper/2608.12342/) (2026)
- [Will sentiment analysis need subculture? A new data augmentation approach](https://mlsanity.com/paper/2309.00178/) (2023)
- [Multi-Label Topic Model for Financial Textual Data](https://mlsanity.com/paper/2311.07598/) (2023)
- [FaiMA: Feature-aware In-context Learning for Multi-domain Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2403.01063/) (2024)
- [FinBloom: Knowledge Grounding Large Language Model with Real-time Financial Data](https://mlsanity.com/paper/2502.18471/) (2025)
- [Impact of LLMs news Sentiment Analysis on Stock Price Movement Prediction](https://mlsanity.com/paper/2602.00086/) (2026)
- [Janus-Q: End-to-End Event-Driven Trading via Hierarchical-Gated Reward Modeling](https://mlsanity.com/paper/2602.19919/) (2026)
- [Cross-LLM Consistency in Inference: Evidence from Shared Interactions](https://mlsanity.com/paper/2606.08129/) (2026)
- [SILC-EFSA: Self-aware In-context Learning Correction for Entity-level Financial Sentiment Analysis](https://mlsanity.com/paper/2412.19140/) (2024)
- [A Pluggable Multi-Task Learning Framework for Sentiment-Aware Financial Relation Extraction](https://mlsanity.com/paper/2506.12452/) (2025)
- [Multi-Modal Opinion Integration for Financial Sentiment Analysis using Cross-Modal Attention](https://mlsanity.com/paper/2512.03464/) (2025)
- [Large-Scale Aspect-Based Sentiment Analysis with Reasoning-Infused LLMs](https://mlsanity.com/paper/2601.03940/) (2026)

## Abstract (arXiv)

Fine-grained financial sentiment analysis on news headlines is a challenging task requiring human-annotated datasets to achieve high performance. Limited studies have tried to address the sentiment extraction task in a setting where multiple entities are present in a news headline. In an effort to further research in this area, we make publicly available SEntFiN 1.0, a human-annotated dataset of 10,753 news headlines with entity-sentiment annotations, of which 2,847 headlines contain multiple entities, often with conflicting sentiments. We augment our dataset with a database of over 1,000 financial entities and their various representations in news media amounting to over 5,000 phrases. We propose a framework that enables the extraction of entity-relevant sentiments using a feature-based approach rather than an expression-based approach. For sentiment extraction, we utilize 12 different learning schemes utilizing lexicon-based and pre-trained sentence representations and five classification approaches. Our experiments indicate that lexicon-based n-gram ensembles are above par with pre-trained word embedding schemes such as GloVe. Overall, RoBERTa and finBERT (domain-specific BERT) achieve the highest average accuracy of 94.29% and F1-score of 93.27%. Further, using over 210,000 entity-sentiment predictions, we validate the economic effect of sentiments on aggregate market movements over a long duration.