# Forecasting GDP in Europe with Textual Data

arXiv: https://arxiv.org/abs/2401.07179 · HTML: https://mlsanity.com/paper/2401.07179/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 30 references that are in the map.
- First posted on arXiv 14 Jan 2024.

## Builds on (most cited first, 2 of 2)

- [Forecasting with Economic News](https://mlsanity.com/paper/2203.15686/) (2022)
- [News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions](https://mlsanity.com/paper/1801.07047/) (2018)

## Built on by (most cited first, 2 of 2)

- [Sentiment Analysis of Economic Text: A Lexicon-Based Approach](https://mlsanity.com/paper/2411.13958/) (2024)
- [Wage Sentiment Indices Derived from Survey Comments via Large Language Models](https://mlsanity.com/paper/2509.00290/) (2025)

## Abstract (arXiv)

We evaluate the informational content of news-based sentiment indicators for forecasting Gross Domestic Product (GDP) and other macroeconomic variables of the five major European economies. Our data set includes over 27 million articles for 26 major newspapers in 5 different languages. The evidence indicates that these sentiment indicators are significant predictors to forecast macroeconomic variables and their predictive content is robust to controlling for other indicators available to forecasters in real-time.