Forecasting consumer confidence through semantic network analysis of online news

TL;DR

This paper presents a novel approach combining text mining and social network analysis to predict consumer confidence from online news articles, demonstrating strong predictive power and offering an alternative to traditional surveys.

Contribution

It introduces an innovative method for forecasting consumer confidence using semantic network analysis of large-scale online news data, enhancing predictive accuracy and timeliness.

Findings

Semantic importance of economic keywords predicts consumer judgments.

Strong predictive power for current household and national confidence.

Offers a complementary approach to traditional survey methods.

Abstract

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.