Sentiment Analysis in Finance: From Transformers Back to eXplainable Lexicons (XLex)
Journal
arXiv preprint arXiv:2306.03997
Date Issued
2023-06-06
Author(s)
Rizinski, Maryan
Peshov, Hristijan
Jovanovik, Milos
Abstract
Lexicon-based sentiment analysis in finance leverages specialized, manually annotated
lexicons created by human experts to effectively extract sentiment from financial texts. Although lexiconbased methods are simple to implement and fast to operate on textual data, they require considerable
manual annotation efforts to create, maintain, and update the lexicons. These methods are also considered
inferior to the deep learning-based approaches, such as transformer models, which have become dominant
in various natural language processing (NLP) tasks due to their remarkable performance. However, their
efficacy comes at a cost: these models require extensive data and computational resources for both training
and testing. Additionally, they involve significant prediction times, making them unsuitable for real-time
production environments or systems with limited processing capabilities. In this paper, we introduce a novel
methodology named eXplainable Lexicons (XLex) that combines the advantages of both lexicon-based
methods and transformer models. We propose an approach that utilizes transformers and SHapley Additive
exPlanations (SHAP) for explainability to automatically learn financial lexicons. Our study presents four
main contributions. Firstly, we demonstrate that transformer-aided explainable lexicons can enhance the
vocabulary coverage of the benchmark Loughran-McDonald (LM) lexicon. This enhancement leads to a
significant reduction in the need for human involvement in the process of annotating, maintaining, and
updating the lexicons. Secondly, we show that the resulting lexicon outperforms the standard LM lexicon in
sentiment analysis of financial datasets. Thirdly, we illustrate that the lexicon-based approach is significantly
more efficient in terms of model speed and size compared to transformers. Lastly, the proposed XLex
approach is inherently more interpretable than transformer models. This interpretability is advantageous
as lexicon models rely on predefined rules, unlike transformers, which have complex inner workings. The
interpretability of the models allows for better understanding and insights into the results of sentiment
analysis, making the XLex approach a valuable tool for financial decision-making.
lexicons created by human experts to effectively extract sentiment from financial texts. Although lexiconbased methods are simple to implement and fast to operate on textual data, they require considerable
manual annotation efforts to create, maintain, and update the lexicons. These methods are also considered
inferior to the deep learning-based approaches, such as transformer models, which have become dominant
in various natural language processing (NLP) tasks due to their remarkable performance. However, their
efficacy comes at a cost: these models require extensive data and computational resources for both training
and testing. Additionally, they involve significant prediction times, making them unsuitable for real-time
production environments or systems with limited processing capabilities. In this paper, we introduce a novel
methodology named eXplainable Lexicons (XLex) that combines the advantages of both lexicon-based
methods and transformer models. We propose an approach that utilizes transformers and SHapley Additive
exPlanations (SHAP) for explainability to automatically learn financial lexicons. Our study presents four
main contributions. Firstly, we demonstrate that transformer-aided explainable lexicons can enhance the
vocabulary coverage of the benchmark Loughran-McDonald (LM) lexicon. This enhancement leads to a
significant reduction in the need for human involvement in the process of annotating, maintaining, and
updating the lexicons. Secondly, we show that the resulting lexicon outperforms the standard LM lexicon in
sentiment analysis of financial datasets. Thirdly, we illustrate that the lexicon-based approach is significantly
more efficient in terms of model speed and size compared to transformers. Lastly, the proposed XLex
approach is inherently more interpretable than transformer models. This interpretability is advantageous
as lexicon models rely on predefined rules, unlike transformers, which have complex inner workings. The
interpretability of the models allows for better understanding and insights into the results of sentiment
analysis, making the XLex approach a valuable tool for financial decision-making.
Subjects
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