  Large language models (LLMs) play a vital role in almost every domain in
today's organizations. In the context of this work, we highlight the use of
LLMs for sentiment analysis (SA) and explainability. Specifically, we
contribute a novel technique to leverage LLMs as a post-hoc model-independent
tool for the explainability of SA. We applied our technique in the financial
domain for currency-pair price predictions using open news feed data merged
with market prices. Our application shows that the developed technique is not
only a viable alternative to using conventional eXplainable AI but can also be
fed back to enrich the input to the machine learning (ML) model to better
predict future currency-pair values. We envision our results could be
generalized to employing explainability as a conventional enrichment for ML
input for better ML predictions in general.
