Forecasting consumer confidence through semantic network analysis of online news
This provides a method for economists and policymakers to estimate consumer confidence with less reliance on surveys, though it is incremental as it builds on existing text mining and network analysis techniques.
The research tackled forecasting consumer confidence by analyzing semantic networks in online news, using over 1.8 million Italian articles over four years, and found strong predictive power for current economic judgments, offering a complementary approach to traditional surveys.
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.