# News-based Business Sentiment and its Properties as an Economic Index

arXiv: https://arxiv.org/abs/2110.10340 · HTML: https://mlsanity.com/paper/2110.10340/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 59 references that are in the map.
- First posted on arXiv 20 Oct 2021.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Quantifying Attention Flow in Transformers](https://mlsanity.com/paper/2005.00928/) (2020)
- [Is Attention Interpretable?](https://mlsanity.com/paper/1906.03731/) (2019)
- [Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network](https://mlsanity.com/paper/1701.02962/) (2017)
- [Improving Stock Market Prediction via Heterogeneous Information Fusion](https://mlsanity.com/paper/1801.00588/) (2018)
- [Universal, Unsupervised (Rule-Based), Uncovered Sentiment Analysis](https://mlsanity.com/paper/1606.05545/) (2016)
- [S-APIR: News-based Business Sentiment Index](https://mlsanity.com/paper/2003.02973/) (2020)

## Built on by (most cited first, 3 of 3)

- [Framing in the Presence of Supporting Data: A Case Study in U.S. Economic News](https://mlsanity.com/paper/2402.14224/) (2024)
- [Economy Watchers Survey Provides Datasets and Tasks for Japanese Financial Domain](https://mlsanity.com/paper/2407.14727/) (2024)
- [Refined and Segmented Price Sentiment Indices from Survey Comments](https://mlsanity.com/paper/2411.09937/) (2024)

## Abstract (arXiv)

This paper presents an approach to measuring business sentiment based on textual data. Business sentiment has been measured by traditional surveys, which are costly and time-consuming to conduct. To address the issues, we take advantage of daily newspaper articles and adopt a self-attention-based model to define a business sentiment index, named S-APIR, where outlier detection models are investigated to properly handle various genres of news articles. Moreover, we propose a simple approach to temporally analyzing how much any given event contributed to the predicted business sentiment index. To demonstrate the validity of the proposed approach, an extensive analysis is carried out on 12 years' worth of newspaper articles. The analysis shows that the S-APIR index is strongly and positively correlated with established survey-based index (up to correlation coefficient r=0.937) and that the outlier detection is effective especially for a general newspaper. Also, S-APIR is compared with a variety of economic indices, revealing the properties of S-APIR that it reflects the trend of the macroeconomy as well as the economic outlook and sentiment of economic agents. Moreover, to illustrate how S-APIR could benefit economists and policymakers, several events are analyzed with respect to their impacts on business sentiment over time.