Enhancing aspect-based financial sentiment analysis through contrastive learning

Abstract

The subject of research in the article explores the specialized application of Aspect-Based Financial Sentiment Analysis (ABFSA), focusing on the intricate and multifaceted emotional landscape of financial textual data. The study extends the current understanding of sentiment analysis by addressing its limitations and opportunities within a financial context. The purpose of the work is to advance the field of Aspect-Based Financial Sentiment Analysis by developing a more nuanced and effective methodology for analyzing sentiments in financial news. Additionally, the study aims to assess the efficacy of recent advancements in Natural Language Processing (NLP) and machine learning for enhancing ABFSA models. The article deals with the following tasks: Firstly, the study focuses on the rigorous pre-processing of the SEntFiN dataset to make it more amenable to advanced machine learning techniques, specifically contrastive learning methodologies. Secondly, it aims to architect a unified model that integrates state-of-the-art machine learning techniques, including DeBERTa v3, contrast learning, and LoRa fine-tuning. Lastly, the research critically evaluates the proposed model's performance metrics across the test dataset and compares them with existing methodologies. The following methods are used: Firstly, the study employs pre-processing techniques tailored for the SEntFiN dataset, which is explicitly designed for entity-sensitive sentiment analysis in financial news. Secondly, it utilizes advanced machine learning techniques such as DeBERTa v3 for language model pre-training, contrast learning for focusing on causal relationships, and LoRa for fine-tuning large language models. Lastly, performance evaluation methods are used to assess the efficacy of the proposed model, including comparisons with existing methodologies in the field.The following results were obtained: The study reveals that the proposed pre-processing framework successfully accommodates the variable number of entities present in financial news, thereby improving the granularity of sentiment classification. Furthermore, the integration of advanced NLP and machine learning techniques significantly enhances the accuracy and efficiency of ABFSA models. Conclusions: The paper concludes that specialized ABFSA methodologies, when augmented with advanced NLP techniques and a robust pre-processing framework, can offer a more nuanced and accurate representation of sentiment in financial narratives. The study lays the groundwork for future research in this nascent yet crucial interdisciplinary field, providing actionable insights for stakeholders ranging from investors to financial analysts.