  This paper presents a novel approach for explainability in financial analysis
by deriving financially-explainable statistical relationships through
aspect-based sentiment analysis, Pearson correlation, Granger causality &
uncertainty coefficient. The proposed methodology involves constructing an
aspect list from financial literature and applying aspect-based sentiment
analysis on social media text to compute sentiment scores for each aspect.
Pearson correlation is then applied to uncover financially explainable
relationships between aspect sentiment scores and stock prices. Findings for
derived relationships are made robust by applying Granger causality to
determine the forecasting ability of each aspect sentiment score for stock
prices. Finally, an added layer of interpretability is added by evaluating
uncertainty coefficient scores between aspect sentiment scores and stock
prices. This allows us to determine the aspects whose sentiment scores are most
statistically significant for stock prices. Relative to other methods, our
approach provides a more informative and accurate understanding of the
relationship between sentiment analysis and stock prices. Specifically, this
methodology enables an interpretation of the statistical relationship between
aspect-based sentiment scores and stock prices, which offers explainability to
AI-driven financial decision-making.
