# Sentiment Analysis of Financial News Articles using Performance Indicators

arXiv: https://arxiv.org/abs/1811.11008 · HTML: https://mlsanity.com/paper/1811.11008/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2018.

## Built on by (most cited first, 6 of 6)

- [Towards an Enhanced Understanding of Bias in Pre-trained Neural Language Models: A Survey with Special Emphasis on Affective Bias](https://mlsanity.com/paper/2204.10365/) (2022)
- [Sentiment Analysis in Finance: From Transformers Back to eXplainable Lexicons (XLex)](https://mlsanity.com/paper/2306.03997/) (2023)
- [Pre-trained Large Language Models for Financial Sentiment Analysis](https://mlsanity.com/paper/2401.05215/) (2024)
- [Contextual Sentence Analysis for the Sentiment Prediction on Financial Data](https://mlsanity.com/paper/2112.13790/) (2021)
- [Automatic Construction of Context-Aware Sentiment Lexicon in the Financial Domain Using Direction-Dependent Words](https://mlsanity.com/paper/2106.05723/) (2021)
- [BTC-SAM: Leveraging LLMs for Generation of Bias Test Cases for Sentiment Analysis Models](https://mlsanity.com/paper/2509.24101/) (2025)

## Abstract (arXiv)

Mining financial text documents and understanding the sentiments of individual investors, institutions and markets is an important and challenging problem in the literature. Current approaches to mine sentiments from financial texts largely rely on domain specific dictionaries. However, dictionary based methods often fail to accurately predict the polarity of financial texts. This paper aims to improve the state-of-the-art and introduces a novel sentiment analysis approach that employs the concept of financial and non-financial performance indicators. It presents an association rule mining based hierarchical sentiment classifier model to predict the polarity of financial texts as positive, neutral or negative. The performance of the proposed model is evaluated on a benchmark financial dataset. The model is also compared against other state-of-the-art dictionary and machine learning based approaches and the results are found to be quite promising. The novel use of performance indicators for financial sentiment analysis offers interesting and useful insights.