Sentiment Analysis on Inflation after Covid-19

Xinyu Li and Zihan Tang

Papers from arXiv.org

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.

Date: 2022-09, Revised 2022-12
New Economics Papers: this item is included in nep-big, nep-cmp and nep-mon
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Published in Applied Economics and Finance 10 (2023) 10-23

Downloads: (external link)
http://arxiv.org/pdf/2209.14737 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2209.14737

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().