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Authors: Himmet Kaplan 1 ; Ralf-Peter Mundani 2 ; Heiko Rölke 2 and Albert Weichselbraun 2

Affiliations: 1 Zurich University of Applied Sciences, Winterthur, Switzerland ; 2 University of Applied Sciences of the Grisons, Chur, Switzerland

Keyword(s): Natural Language Processing, Sentiment Analysis, Transformers, FinBERT, Crude Oil Market, Fine-Tuning.

Abstract: Predicting market movements based on the sentiment of news media has a long tradition in data analysis. With advances in natural language processing, transformer architectures have emerged that enable contextually aware sentiment classification. Nevertheless, current methods built for the general financial market such as FinBERT cannot distinguish asset-specific value-driving factors. This paper addresses this shortcoming by presenting a method that identifies and classifies events that impact supply and demand in the crude oil markets within a large corpus of relevant news headlines. We then introduce CrudeBERT, a new sentiment analysis model that draws upon these events to contextualize and fine-tune FinBERT, thereby yielding improved sentiment classifications for headlines related to the crude oil futures market. An extensive evaluation demonstrates that CrudeBERT outperforms proprietary and open-source solutions in the domain of crude oil.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Kaplan, H. ; Mundani, R. ; Rölke, H. and Weichselbraun, A. (2023). CrudeBERT: Applying Economic Theory Towards Fine-Tuning Transformer-Based Sentiment Analysis Models to the Crude Oil Market. In Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-648-4; ISSN 2184-4992, SciTePress, pages 324-334. DOI: 10.5220/0011749600003467

@conference{iceis23,
author={Himmet Kaplan and Ralf{-}Peter Mundani and Heiko Rölke and Albert Weichselbraun},
title={CrudeBERT: Applying Economic Theory Towards Fine-Tuning Transformer-Based Sentiment Analysis Models to the Crude Oil Market},
booktitle={Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2023},
pages={324-334},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011749600003467},
isbn={978-989-758-648-4},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - CrudeBERT: Applying Economic Theory Towards Fine-Tuning Transformer-Based Sentiment Analysis Models to the Crude Oil Market
SN - 978-989-758-648-4
IS - 2184-4992
AU - Kaplan, H.
AU - Mundani, R.
AU - Rölke, H.
AU - Weichselbraun, A.
PY - 2023
SP - 324
EP - 334
DO - 10.5220/0011749600003467
PB - SciTePress

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