@inproceedings{rezende-etal-2026-enhancing,
title = "Enhancing {B}razilian Inflation Forecasts through Sentiment Analysis Using Large Language Models",
author = "Rezende, Lucas Miranda Mendon{\c{c}}a and
Junior, C{\'e}zio Luiz Ferreira and
Machado, Mateus Tarcinalli and
Ruiz, Evandro Eduardo Seron",
editor = "Souza, Marlo and
de-Dios-Flores, Iria and
Santos, Diana and
Freitas, Larissa and
Souza, Jackson Wilke da Cruz and
Ribeiro, Eug{\'e}nio",
booktitle = "Proceedings of the 17th International Conference on Computational Processing of {P}ortuguese ({PROPOR} 2026) - Vol. 1",
month = apr,
year = "2026",
address = "Salvador, Brazil",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.propor-1.17/",
pages = "172--182",
ISBN = "979-8-89176-387-6",
abstract = "Reliable inflation forecasts play a critical role in economic stability and policy decisions. Traditional econometric models perform well but often overlook qualitative signals that could improve predictive accuracy. Recent advances in AI-based Natural Language Processing enable the extraction of latent sentiment, offering a promising avenue for inflation forecasting. This study proposes a framework that combines Large Language Models (LLMs) to extract sentiment variables from the Brazilian Monetary Policy Committee (COPOM) minutes, optimize bias to match human-collected sentiment, and integrate them into ARIMA and LSTM models for one-step-ahead monthly IPCA prediction. Results show that LLM-generated sentiment trends are temporally coherent with historical inflation patterns and highly statistically significant (p {\ensuremath{<}} 0.001). Models whose sentiment evaluations aligned more closely with human assessments (e.g., grok-4-fast and llama-4-maverick) achieved superior forecasting performance. ARIMA models consistently benefited from sentiment inclusion, while LSTM results were more variable."
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<abstract>Reliable inflation forecasts play a critical role in economic stability and policy decisions. Traditional econometric models perform well but often overlook qualitative signals that could improve predictive accuracy. Recent advances in AI-based Natural Language Processing enable the extraction of latent sentiment, offering a promising avenue for inflation forecasting. This study proposes a framework that combines Large Language Models (LLMs) to extract sentiment variables from the Brazilian Monetary Policy Committee (COPOM) minutes, optimize bias to match human-collected sentiment, and integrate them into ARIMA and LSTM models for one-step-ahead monthly IPCA prediction. Results show that LLM-generated sentiment trends are temporally coherent with historical inflation patterns and highly statistically significant (p \ensuremath< 0.001). Models whose sentiment evaluations aligned more closely with human assessments (e.g., grok-4-fast and llama-4-maverick) achieved superior forecasting performance. ARIMA models consistently benefited from sentiment inclusion, while LSTM results were more variable.</abstract>
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%0 Conference Proceedings
%T Enhancing Brazilian Inflation Forecasts through Sentiment Analysis Using Large Language Models
%A Rezende, Lucas Miranda Mendonça
%A Junior, Cézio Luiz Ferreira
%A Machado, Mateus Tarcinalli
%A Ruiz, Evandro Eduardo Seron
%Y Souza, Marlo
%Y de-Dios-Flores, Iria
%Y Santos, Diana
%Y Freitas, Larissa
%Y Souza, Jackson Wilke da Cruz
%Y Ribeiro, Eugénio
%S Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 1
%D 2026
%8 April
%I Association for Computational Linguistics
%C Salvador, Brazil
%@ 979-8-89176-387-6
%F rezende-etal-2026-enhancing
%X Reliable inflation forecasts play a critical role in economic stability and policy decisions. Traditional econometric models perform well but often overlook qualitative signals that could improve predictive accuracy. Recent advances in AI-based Natural Language Processing enable the extraction of latent sentiment, offering a promising avenue for inflation forecasting. This study proposes a framework that combines Large Language Models (LLMs) to extract sentiment variables from the Brazilian Monetary Policy Committee (COPOM) minutes, optimize bias to match human-collected sentiment, and integrate them into ARIMA and LSTM models for one-step-ahead monthly IPCA prediction. Results show that LLM-generated sentiment trends are temporally coherent with historical inflation patterns and highly statistically significant (p \ensuremath< 0.001). Models whose sentiment evaluations aligned more closely with human assessments (e.g., grok-4-fast and llama-4-maverick) achieved superior forecasting performance. ARIMA models consistently benefited from sentiment inclusion, while LSTM results were more variable.
%U https://aclanthology.org/2026.propor-1.17/
%P 172-182
Markdown (Informal)
[Enhancing Brazilian Inflation Forecasts through Sentiment Analysis Using Large Language Models](https://aclanthology.org/2026.propor-1.17/) (Rezende et al., PROPOR 2026)
ACL