# Sentence-Level Sentiment Analysis of Financial News Using Distributed Text Representations and Multi-Instance Learning

arXiv: https://arxiv.org/abs/1901.00400 · HTML: https://mlsanity.com/paper/1901.00400/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2018.

## Built on by (most cited first, 4 of 4)

- [Financial Sentiment Analysis on News and Reports Using Large Language Models and FinBERT](https://mlsanity.com/paper/2410.01987/) (2024)
- [Pre-trained Large Language Models for Financial Sentiment Analysis](https://mlsanity.com/paper/2401.05215/) (2024)
- [Integrating Floor Plans into Hedonic Models for Rent Price Appraisal](https://mlsanity.com/paper/2102.08162/) (2021)
- [Evaluating Financial Sentiment Analysis with Annotators Instruction Assisted Prompting: Enhancing Contextual Interpretation and Stock Prediction Accuracy](https://mlsanity.com/paper/2505.07871/) (2025)

## Abstract (arXiv)

Researchers and financial professionals require robust computerized tools that allow users to rapidly operationalize and assess the semantic textual content in financial news. However, existing methods commonly work at the document-level while deeper insights into the actual structure and the sentiment of individual sentences remain blurred. As a result, investors are required to apply the utmost attention and detailed, domain-specific knowledge in order to assess the information on a fine-grained basis. To facilitate this manual process, this paper proposes the use of distributed text representations and multi-instance learning to transfer information from the document-level to the sentence-level. Compared to alternative approaches, this method features superior predictive performance while preserving context and interpretability. Our analysis of a manually-labeled dataset yields a predictive accuracy of up to 69.90%, exceeding the performance of alternative approaches by at least 3.80 percentage points. Accordingly, this study not only benefits investors with regard to their financial decision-making, but also helps companies to communicate their messages as intended.