Sentiment Analysis on Inflation after Covid-19

TL;DR

This paper develops a Twitter-based sentiment analysis method using deep learning to predict inflation perceptions, demonstrating its effectiveness in capturing regional and temporal inflation patterns post-Covid-19.

Contribution

It introduces a novel approach combining Twitter data and deep learning, specifically BERT, to measure and predict inflation sentiment with high frequency and regional detail.

Findings

Twitter sentiment correlates strongly with inflation indices

BERT outperforms traditional machine learning models in prediction accuracy

Twitter-based sentiment analysis provides timely inflation perception indicators

Abstract

We implement traditional machine learning and deep learning methods for global tweets from 2017-2022 to build a high-frequency measure of the public's sentiment index on inflation and analyze its correlation with other online data sources such as google trend and market-oriented inflation index. We use manually labeled trigrams to test the prediction performance of several machine learning models(logistic regression,random forest etc.) and choose Bert model for final demonstration. Later, we sum daily tweets' sentiment scores gained from Bert model to obtain the predicted inflation sentiment index, and we further analyze the regional and pre/post covid patterns of these inflation indexes. Lastly, we take other empirical inflation-related data as references and prove that twitter-based inflation sentiment analysis method has an outstanding capability to predict inflation. The results suggest that Twitter combined with deep learning methods can be a novel and timely method to utilize existing abundant data sources on inflation expectations and provide daily indicators of consumers' perception on inflation.