本研究以系統功能語言學（Systemic Functional Linguistics, SFL）的評價理論（Appraisal Theory）為基礎，探討台灣 PTT 八卦板中針對政黨的顯性評價語言，並嘗試結合大型語言模型（Large Language Model, LLM）之提示學習（prompting）方法進行自動化分類。研究聚焦於 Judgement 類別的五個子分類（normality、capacity、tenacity、propriety、veracity），針對民進黨與國民黨兩個主要政黨的評論語料進行標註與分析。方法上，首先以人工標註建立資料集，接著使用 zero-shot 與 few-shot 提示方法，評估 GPT 模型在自動判斷 Judgement 子類別的準確度，並進行錯誤分析。研究同時設計了一個互動式資料儀表板，整合趨勢視覺化與文字雲模組，協助使用者觀察不同時間區段、政黨與子類別的評價趨勢。結果顯示，few-shot 模式在整體表現上優於 zero-shot 模式。結合趨勢視覺化與文字雲發現，民進黨相關評論中於六月到八月之間，道德相關（propriety）負面評價上升，其評價亦與 2023 年台灣 #MeToo 運動有密切關係，而國民黨則集中受到能力相關（capacity）負面評價，其評價與延後提名過程有關。研究結果顯示 LLM 在輔助政治語料分析上具有潛力，但仍受限於多歧義理解與隱喻辨識能力。

This study is grounded in Appraisal framework within Systemic Functional Linguistics (SFL) and investigates explicit evaluative language directed at political parties on Taiwan's PTT Gossiping board. It explores the integration of prompting techniques from Large Language Models (LLMs) to automate classification. The research focuses on five subcategories under the Judgement: normality, capacity, tenacity, propriety, and veracity, and annotates the comments manually. Later, according to the statistical results, this study analyzes comment corpora related to Taiwan's two major political parties: the Democratic Progressive Party (DPP) and the Kuomintang (KMT). Methodologically, the study first constructs a manually annotated dataset, then applies zero-shot and few-shot prompting approaches to evaluate the performance of GPT models in identifying Judgement subcategories, followed by error analysis. An interactive visualization dashboard was also developed, combining trend visualizations and word cloud to help users observe evaluative patterns across time periods, parties, and subcategories. Results show that the few-shot approach outperforms the zero-shot method overall. Through trend visualization and word cloud analysis, the study finds that negative evaluations of Propriety targeting the DPP increased between June and August, closely related to Taiwan's 2023 #MeToo movement. In contrast, the KMT received concentrated negative evaluations of Capacity, associated with delays in its nomination process. The findings suggest that LLMs hold promise for assisting in political discourse analysis, though limitations remain in ambiguity understanding and metaphor recognition.

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