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(2023): Integrating Economic Theory, Domain Knowledge, and Social Knowledge into Hybrid Sentiment Models for Predicting Crude Oil Markets. In: Cognitive Computation, zuletzt geprüft am 31.03.2023
Abstract: For several decades, sentiment analysis has been considered a key indicator for assessing market mood and predicting future price changes. Accurately predicting commodity markets requires an understanding of fundamental market dynamics such as the interplay between supply and demand, which are not considered in standard affective models. This paper introduces two domain-specific affective models, CrudeBERT and CrudeBERT+, that adapt sentiment analysis to the crude oil market by incorporating economic theory with common knowledge of the mentioned entities and social knowledge extracted from Google Trends. To evaluate the predictive capabilities of these models, comprehensive experiments were conducted using dynamic time warping to identify the model that best approximates WTI crude oil futures price movements. The evaluation included news headlines and crude oil prices between January 2012 and April 2021. The results show that CrudeBERT+ outperformed RavenPack, BERT, FinBERT, and early CrudeBERT models during the 9-year evaluation period and within most of the individual years that were analyzed. The success of the introduced domain-specific affective models demonstrates the potential of integrating economic theory with sentiment analysis and external knowledge sources to improve the predictive power of financial sentiment analysis models. The experiments also confirm that CrudeBERT+ has the potential to provide valuable insights for decision-making in the crude oil market.
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(2021): Understanding and Exploiting Deep Learning-based Sentiment Analysis from News Headlines for Predicting Price Movements of WTI Crude Oil. Masterarbeit Information and Data Management. Fachhochschule Graubünden, Chur. Schweizerisches Institut für Informationswissenschaft (SII).
Abstract: Deep learning has become a popular approach for sentiment analysis, a prevalent text classification task in natural language processing. It has been demonstrated to outperform tradition-al classification methods bringing enormous potential in various applications. This thesis aims to research and develop models that can perform sentiment analysis on the news related to crude oil and provide valuable insight. First, a literature review is provided on the potentials of news affecting crude oil prices and the current state-of-the-art deep learning-based sentiment analysis methods utilizing transformer architectures. Additionally, news data sources, as well as appropriate frameworks for conducting sentiment analysis, are examined. Next, based on the literature review, the models are identified, implemented and iteratively fine-tuned through domain adaptation. Finally, recommendations for implementing deep learning-based sentiment analysis methods for predicting crude oil prices are made.