Predicting Consumer Confidence Index Using Social Media Sentiment Analysis

Abstract

The Consumer Confidence Index (CCI) serves as a critical economic indicator, yet traditional survey-based methods for its measurement often entail delays and high costs. This study explores the potential of leveraging real-time social media data as an alternative approach to predict CCI trends. By employing sentiment analysis on a large dataset of user-generated content from platforms such as Twitter and Reddit, this research quantifies public sentiment and examines its correlation with official CCI values. A regression-based predictive model was developed, incorporating sentiment scores alongside macroeconomic variables for enhanced accuracy. The findings reveal a statistically significant relationship between aggregated social media sentiment and CCI movements, with the model demonstrating robust predictive performance, particularly in capturing short-term fluctuations. These results underscore the value of social media as a timely and cost-effective supplementary tool for forecasting consumer confidence. The study contributes to the growing body of literature on non-traditional data sources in economic forecasting and offers practical implications for policymakers and businesses seeking to anticipate economic trends.