## 1 Introduction  
With the flourishing popularity of social media, researchers are striving to create innovative financial applications that leverage the substantial volume of news and reviews. An essential aspect of these endeavors involves applying sentiment analysis (SA) to assess the impact of sentiment on stock market fluctuations. Numerous instances illustrate the impact of social media reviews on the movement of stocks [1]. Remarkably, a single comment or retweet from a prominent internet figure can lead to significant fluctuations in a listed company's stock price within days, prompting public responses to counteract adverse social media sentiment. Consequently, sentiment analysis has become increasingly important in the financial domain following seminal work that explored the asymmetric and affective effects of news on financial market volatility [2]. Different forms of textual information, ranging from news articles and tweets to commentaries and corporate disclosures, have been applied in various financial research studies. However, unlike sentiment analysis in other fields [3], the goal of sentiment analysis in the financial field is more complex. To establish a robust foundation for market prediction models, analysts not only aim to extract sentiment polarity from texts but also to comprehend the underlying rationale behind an article's judgment. They are interested in identifying the specific financial concepts or arguments that drive the sentiment, which can act as confidence indicators that either reflect subjective human judgments or correlate with objective market movements. Indeed, predicting the price of a chaotic system such as the stock market is inherently challenging, especially in the short term. Analysis of financial media commentaries related to securities drawn from a real-world dataset covering the past two decades has revealed that the consistency between media recommendations and subsequent stock price movements is less than one-third. Hence, even experienced securities analysts who devote considerable time to analyzing diverse financial news and social media comments often treat such commentary as referential opinions rather than absolute truths. Specifically, the arguments presented within these comments and the financial reasoning they embody hold more value than the summarized conclusions. Thus, the value of SA in the financial domain goes beyond merely providing highly accurate sentiment classification outcomes [4]; it critically includes the capability to offer interpretable concept-level explanations that reveal the 'why' behind a judgment, supporting the overall sentiment direction of documents. This aspect is crucial for financial analysts, who often rely on such analyses to streamline the process of identifying sentiment trends in content related to securities. It also provides them with a structured and domain-specific foundational rationale for their evaluations. Consequently, the credibility of outcomes from the sentiment analysis model is substantially increased, particularly when the model is pivotal in financial decision-making processes. For the purpose of enhancing the explainability of sentiment classification models in the financial domain, this study proposes a novel approach that integrates financial domain knowledge into a hierarchical model. Inspired by the foundational BERT [5] model and the hierarchical attention structure involving word- and sentence-level queries as described in FISHQA [6], this work introduces a Query-Guided Dual Attention (QGDA) mechanism within a hierarchical BERT-GRU architecture. Unlike traditional BERT models commonly employed in text classification, our model is specifically designed to advance the explainability of classification decisions by incorporating this query-driven attention. These queries are derived from securities knowledge and act as explicit guides, directing the model's attention to text segments relevant to predefined financial concepts.