## 2 Related Work
### Explainable Sentiment Analysis Network  
As explained above, sentiment analysis methods which consider only the polarity or classification of the text sentiment are insufficient in the financial field, leading to our research on explainable sentiment analysis. Explanation and interpretation are ambiguous concepts, especially for the black-box models resulting from deep learning-based methods. On the other hand, aspect-based sentiment analysis (ABSA) [32] aims to determine which specific aspects are mentioned in the comments and to assess their corresponding emotional polarity, while explainable sentiment analysis [33] aims to provide explainable justifications for the decisions in some level of detail. In research of eXplainable Artificial Intelligence (XAI), the term \"explainability\" is not well defined and often has different research goals with different approaches [34]. Nonetheless, all of these goals and approaches fall under the overall concept of explainability, which seeks to augment the training process, learned representations, and decisions with human-interpretable explanations [35]. For example, interpretable local surrogate-based approaches such as LIME [36] aim to explain classifiers' results by replacing the decision function with a local self-explanatory model. Occlusion analysis-based methods aim to repeatedly test the effect of occluding patches or individual features on the neural network output [37]. In one instance, SHAP [38] provides practical algorithms to approximate Shapley values, which can increase model transparency. Gradient-based techniques provide explanations by integrating the gradient [39]. Finally, layerwise relevance propagation (LRP) [40] iteratively utilizes the layered structure of the neural network to provide explanations. To achieve the best possible explainability of sentiment analysis models, the interpretability of attention mechanisms has garnered increased attention. By manipulating the attention distribution and reducing the highest attention value to zero, ref. [41] were able to investigate the interpretability of the attention mechanism. Similarly, ref. [42] employed an adversarial generation method to scrutinize the role of attention distribution, positing that the model's prediction pathway is not singular. For financial text sentiment classification, ref. [6] proposed a specialized hierarchical model that utilizes a set of custom queries as part of a distinctive hierarchical attention mechanism. This model offers a degree of interpretability through the distribution of attention weights among the queries while enhancing classification accuracy for financial documents. Recent works in the domain of sentiment classification have made strides in explainable research. The study by [43] introduced a novel Information Bottleneck-based Gradient (IBG) explanation framework that significantly enhances model interpretability by isolating sentiment-aware features through a focused refinement of word embeddings on intrinsic dimensions. In analyzing sentiment on social media, the research by [44] employed graph neural network embeddings and machine learning algorithms to classify sentiments about ChatGPT using a dataset of 8202 manually labeled tweets. This work notably advances model transparency using gSHapley Additive exPlanations (SHAP), establishing a robust framework for explainable AI in assessing public perceptions of technological progress. Moreover, ref. [45] introduced eXplainable Lexicons (XLex), a method that merges lexicon-based analysis with the capabilities of transformer models for financial sentiment analysis, resulting in improved accuracy, efficiency, and interpretability. However, ref. [46] demonstrated that diminishing the attention weight of specified tokens during training can cause models to generate outputs akin to their original predictions. This suggests a potential risk of misinterpretation or manipulation when relying on attention weights as a basis for judgment.