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Link to original content: https://doi.org/10.48550/arXiv.2305.06140
[2305.06140] CrudeBERT: Applying Economic Theory towards fine-tuning Transformer-based Sentiment Analysis Models to the Crude Oil Market
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Computer Science > Information Retrieval

arXiv:2305.06140 (cs)
[Submitted on 10 May 2023]

Title:CrudeBERT: Applying Economic Theory towards fine-tuning Transformer-based Sentiment Analysis Models to the Crude Oil Market

Authors:Himmet Kaplan, Ralf-Peter Mundani, Heiko Rölke, Albert Weichselbraun
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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.
Subjects: Information Retrieval (cs.IR); Machine Learning (cs.LG)
ACM classes: H.3; H.4; I.2.7
Cite as: arXiv:2305.06140 [cs.IR]
  (or arXiv:2305.06140v1 [cs.IR] for this version)
  https://doi.org/10.48550/arXiv.2305.06140
Journal reference: Proceedings of the 25th International Conference on Enterprise Information Systems (ICEIS 2023), pages 324-334
Related DOI: https://doi.org/10.5220/0011749600003467

Submission history

From: Albert Weichselbraun [view email]
[v1] Wed, 10 May 2023 13:42:56 UTC (6,212 KB)
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