Role-Guided Annotation and Prototype-Aligned Representation Learning for Historical Literature Sentiment Classification (2025)

Abstract: Sentiment analysis of historical literature provides valuable insights for humanities research, yet remains challenging due to scarce annotations and limited generalization of models trained on modern texts. Prior work has primarily focused on two directions: using sentiment lexicons or leveraging large language models (LLMs) for annotation. However, lexicons are often unavailable for historical texts due to limited linguistic resources, and LLM-generated labels often reflect modern sentiment norms and fail to capture the implicit, ironic, or morally nuanced expressions typical of historical literature, resulting in noisy supervision. To address these issues, we introduce a role-guided annotation strategy that prompts LLMs to simulate historically situated perspectives when labeling sentiment. Furthermore, we design a prototype-aligned framework that learns sentiment prototypes from high-resource data and aligns them with low-resource representations via symmetric contrastive loss, improving robustness to noisy labels. Experiments across multiple historical literature datasets show that our method outperforms state-of-the-art baselines, demonstrating its effectiveness.

## introduction sentiment analysis of historical literature reveals the emotional and ideological underpinnings of past societies and provides insight into how their worldviews have shaped the present, thus highlighting the potential of computational methods to bridge cultural history and nlp @xcite . however, this task is challenging due to the scarcity of annotated data, as accurate labeling requires expertise in linguistics, literature, and historical context @xcite . furthermore, models trained on modern texts often struggle to generalize to historical domains due to the use of indirect, ironic, and morally nuanced sentiment expressions @xcite . prior work on historical sentiment analysis has mainly followed two directions. the first relies on sentiment lexicons @xcite @xcite , which map words to labels, are interpretable and easy to apply. however, it is often unavailable for historically under-represented texts due to the limited linguistic resources @xcite . the second direction utilizes large language models (llms) to generate sentiment labels in lowresource settings, which are then used to fine-tune downstream classifiers @xcite . while llm-based annotation shows promise, it faces two main limitations. first, most promptbased annotation methods typically rely on direct instruction prompts @xcite (e.g., "choose the sentiment of the given text") to generate sentiment labels. however, such prompting strategies implicitly assume that sentiment can be interpreted uniformly across time, culture, and context. this assumption often does not hold in historical literature, where sentiment is conveyed implicitly and shaped by rhetorical and moral complexity @xcite , often causing llms misread or overlook sentiment cues in historical texts. in addition, existing works on historical sentiment analysis @xcite typically assumes that the labels produced by llms are reliable, and thus directly uses them for downstream training. however, llm-generated labels are inherently noisy, especially for subtle or stylistically complex expressions in historical literature. this limits the generalizability of downstream models. to address these two major challenges, unlike prior work that either applies sentiment lexicons or uses llms without contextual guidance and consideration of label noise, we propose a role-guided annotation and prototype-aligned representation learning method (rapa). to be concrete, we first introduce a role-play annotation strategy that instructs llms to simulate historical figures and evaluate sentiment according to the ways sentiment was typically conveyed in that historical period. this role-guided approach encourages llms to better capture the sentiment in historical literature and avoid misreading them through a modern lens. second, we propose a unified framework that learns trainable sentiment prototypes from highresource data. these prototypes are class-level representations that capture the core semantics of each sentiment category. we further align them with low-resource prototypes through a symmetric contrastive loss, which helps the model reduce its reliance on potentially noisy labels. even when the label of a historical text is inaccurate or ambiguous, the model can still learn a meaningful representation by being guided toward the correct prototype based on its semantic content. our contributions are summarized as follows: ## methodology in this section, we first define the task of sentiment analysis of historical literature. then, we provide an overview of our proposed framework, rapa, followed by a detailed description of its two main components: role-guided annotation strategy and prototype-aligned representation learning. ## conclusion we propose a novel approach rapa, a role-guided and prototype-aligned framework for sentiment analysis of historical literature. to address the limitations of the llms annotation strategy when applied to culturally and temporally distant texts, we first utilize character-specific cues from chatgpt-4o to generate sentiment labels from a historical perspective. recognizing that llm-generated annotations often contain noise-particularly in low-resource settings-we introduce a prototypealigned representation learning method that denoises training signals by aligning low-resource representations with stable high-resource prototypes. this joint alignment process not only mitigates the impact of unreliable labels but also enhances the structural consistency of sentiment representations across domains. our approach demonstrates the potential of combining role-guided annotation with semantic-level denoising to robustly analyze sentiment in underexplored historical corpora. limitations although the impressive performance of rapa, several limitations point to promising directions for future work. first, our use of chatgpt to annotate data by role-playing historical figures. for different datasets, we manually design customized prompts, which limits the automation of the annotation process. second, due to dataset constraints, our study focuses on a limited set of historical languages and genres. the generalizability of rapa to broader historical corpora and underrepresented languages remains an open and valuable area for exploration. third, our current framework separates cue design from model training without jointly optimizing them. while this modularity simplifies analysis, future research could benefit from jointly optimizing annotation cues and representation learning to further enhance model performance and adaptability.