# Financial Aspect-Based Sentiment Analysis using Deep Representations

arXiv: https://arxiv.org/abs/1808.07931 · HTML: https://mlsanity.com/paper/1808.07931/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2018.

## Built on by (most cited first, 5 of 5)

- [WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain](https://mlsanity.com/paper/2211.00083/) (2022)
- [A Survey on Aspect-Based Sentiment Classification](https://mlsanity.com/paper/2203.14266/) (2022)
- [A Comparative Analysis of Instruction Fine-Tuning LLMs for Financial Text Classification](https://mlsanity.com/paper/2411.02476/) (2024)
- [FinXABSA: Explainable Finance through Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2303.02563/) (2023)
- [SentinelRAG: Synthetic Sentinel Knowledge for RAG Database Copyright Protection](https://mlsanity.com/paper/2606.05787/) (2026)

## Abstract (arXiv)

The topic of aspect-based sentiment analysis (ABSA) has been explored for a variety of industries, but it still remains much unexplored in finance. The recent release of data for an open challenge (FiQA) from the companion proceedings of WWW '18 has provided valuable finance-specific annotations. FiQA contains high quality labels, but it still lacks data quantity to apply traditional ABSA deep learning architecture. In this paper, we employ high-level semantic representations and methods of inductive transfer learning for NLP. We experiment with extensions of recently developed domain adaptation methods and target task fine-tuning that significantly improve performance on a small dataset. Our results show an 8.7% improvement in the F1 score for classification and an 11% improvement over the MSE for regression on current state-of-the-art results.