This paper presents a novel approach for explainability in financial analysis by utilizing the Pearson correlation coefficient to establish a relationship between aspect-based sentiment analysis and stock prices. The proposed methodology involves constructing an aspect list from financial news articles and analyzing sentiment intensity scores for each aspect. These scores are then compared to the stock prices for the relevant companies using the Pearson coefficient to determine any significant correlations. The results indicate that the proposed approach provides a more detailed and accurate understanding of the relationship between sentiment analysis and stock prices, which can be useful for investors and financial analysts in making informed decisions. Additionally, this methodology offers a transparent and interpretable way to explain the sentiment analysis results and their impact on stock prices. Overall, the findings of this paper demonstrate the importance of explainability in financial analysis and highlight the potential benefits of utilizing the Pearson coefficient for analyzing aspect-based sentiment analysis and stock prices. The proposed approach offers a valuable tool for understanding the complex relationships between financial news sentiment and stock prices, providing a new perspective on the financial market and aiding in making informed investment decisions.

翻译：本文提出了一种新颖的金融分析可解释性方法，利用皮尔逊相关系数建立基于方面情感分析与股票价格之间的关系。所提出的方法包括从金融新闻文章中构建方面列表，并分析每个方面的情感强度评分。随后，将这些评分与相关公司的股票价格通过皮尔逊系数进行比较，以确定是否存在显著相关性。结果表明，所提出的方法能够更详细、更准确地理解情感分析与股票价格之间的关系，有助于投资者和金融分析师做出明智决策。此外，该方法提供了一种透明且可解释的方式，用以解释情感分析结果及其对股票价格的影响。总体而言，本文的研究结果证明了可解释性在金融分析中的重要性，并凸显了利用皮尔逊系数分析基于方面情感分析与股票价格的潜在益处。所提出的方法为理解金融新闻情感与股票价格之间的复杂关系提供了有价值的工具，为金融市场提供了新视角，并有助于做出明智的投资决策。