  Lexicon-based sentiment analysis (SA) in finance leverages specialized,
manually annotated lexicons created by human experts to extract sentiment from
financial texts. Although lexicon-based 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 NLP tasks due to their remarkable
performance. However, transformers 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 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, reducing the human involvement in annotating, maintaining, and
updating the lexicons. Secondly, we show that the resulting lexicon outperforms
the standard LM lexicon in SA 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 XLex approach is
inherently more interpretable than transformer models as lexicon models rely on
predefined rules, allowing for better insights into the results of SA and
making the XLex approach a viable tool for financial decision-making.
