# Forecasting consumer confidence through semantic network analysis of online news

arXiv: https://arxiv.org/abs/2105.04900 · HTML: https://mlsanity.com/paper/2105.04900/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 79 references that are in the map.
- First posted on arXiv 11 May 2021.

## Builds on (most cited first, 4 of 4)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distinctiveness Centrality in Social Networks](https://mlsanity.com/paper/1912.03391/) (2019)
- [Brand Intelligence Analytics](https://mlsanity.com/paper/2001.11479/) (2020)
- [Forecasting financial markets with semantic network analysis in the COVID-19 crisis](https://mlsanity.com/paper/2009.04975/) (2020)

## Built on by (most cited first, 1 of 1)

- [Brand Network Booster: A new system for improving brand connectivity](https://mlsanity.com/paper/2309.16228/) (2023)

## Abstract (arXiv)

This research studies the impact of online news on social and economic consumer perceptions through semantic network analysis. Using over 1.8 million online articles on Italian media covering four years, we calculate the semantic importance of specific economic-related keywords to see if words appearing in the articles could anticipate consumers' judgments about the economic situation and the Consumer Confidence Index. We use an innovative approach to analyze big textual data, combining methods and tools of text mining and social network analysis. Results show a strong predictive power for the judgments about the current households and national situation. Our indicator offers a complementary approach to estimating consumer confidence, lessening the limitations of traditional survey-based methods.