## 2 Related Work
### Sentiment Analysis for Financial Articles  
Sentiment analysis aims to compute opinions, sentiments, and subjectivity from text [13]. It can be applied at three levels based on the analyzed text unit: document level, sentence level, or aspect level [14]. The most commonly used approaches include lexicon-based, machine learning, and hybrid approaches [15]. To handle the massive unstructured data accumulated on social media, deep learning-based methods are gaining increasing attention and achieving state-of-the-art results on various tasks [16]. For example, ref. [17] demonstrated the effectiveness of integrating BERT and deep CNN for sentiment analysis of COVID-19 tweets, achieving exceptional performance. Additionally, ref. [18] discussed the use of Gephi and R for mining and visualizing social media and DBpedia data, highlighting the importance of graph-based analysis in understanding data structures. In the financial field, studies have applied deep learning methods to the stock market [19] and examined the effects of financial related articles such as economic news and social media reviews on stock movements. For example, ref. [20] used tweet comments to analyze stock fluctuations over time. In [21], the authors used data from Chinese financial news websites such as Dongfang Fortune and Sina Finance to capture public sentiment toward companies through the comment sections of these financial websites, discovering that the combination of public sentiment and financial news is a good predictor of stock trends. In [22], the authors conducted a comparative analysis of LSTM and GRU models for stock market forecasting under similar conditions. In [23], a topic model to predict stock price movement was constructed using sentiments on social media. Through the analysis of news about real estate related listed companies, ref. [24] not only argued for the impact of news optimism or pessimism on the stock price of the real estate sector but also found that such news could be used predict the volatility of the stock market as much as 14 days before it was released. In [25; 26] the authors further incorporated transformer and BERT for stock price prediction to enhance predictive performance. Following the initial exploration of deep learning's impact on financial markets, the rise of large language models (LLMs) such as ChatGPT (GPT-3.5) [27] and GPT-4 [28] has redefined state-of-the-art benchmarks across numerous fields [29]. However, while LLMs exhibit impressive general-purpose capabilities, their application in the high-stakes financial domain presents significant challenges. These include high computational costs that hinder rapid and widespread deployment as well th in inherent \"black-box\" nature of such models, which conflicts with the critical need for transparent and verifiable reasoning in financial decision-making. This gap highlights a crucial tradeoff between raw performance and practical utility. Our QGDA framework is designed to address this gap by offering a lightweight, cost-effective, and inherently interpretable alternative. Its specialized architecture is particularly well suited for deployment in environments with sensitive or proprietary data, where reliance on large-scale external APIs is not feasible. More importantly, QGDA provides not just sentiment prediction but concept-level explanation by design, revealing the 'why' behind its conclusions. Furthermore, we envision a powerful synergy in which our QGDA model complements rather than competes with LLMs. The challenge of mitigating hallucinations and grounding LLMs in factual domain-specific knowledge is a prominent research area that is often addressed through retrieval-augmented generation (RAG) frameworks [30]. As demonstrated in fields such as fact-checking [31], leveraging structured knowledge can significantly enhance LLM reliability. In this context, our QGDA model can serve as an intelligent and domain-aware retrieval component.