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題名 基於大型語言模型分析2024年美國總統大選期間川普社群媒體發文與比特幣關係研究
The Study of the Relationship Between Trump's Social Media Posts and Bitcoin Returns During the 2024 U.S. Presidential Election with A Large Language Model Analysis
作者 柯昱安
Cahiadharma, Ignatius Harry
貢獻者 卞中佩<br>羅秉政
Pien, Chung-Pei<br>Kendro Vincent
柯昱安
Ignatius Harry Cahiadharma
關鍵詞 情緒分析
自然語言處理（NLP)
大型語言模型（LLMs）
Truth Social
比特幣報酬
政治傳播
加密貨幣敘事
時間序列分析
OLS 回歸
Python
R
大數據分析
Sentiment Analysis
Natural Language Processing (NLP)
Large Language Models (LLMs)
Truth Social
Bitcoin Returns
Political Communication
Cryptocurrency Narratives
Time Series Analysis
OLS Regression
Python
R
Big Data analysis
日期 2025
上傳時間 4-Aug-2025 14:18:47 (UTC+8)
摘要 鑑於2024年美國總統⼤選結果對於美國及全球的經濟將有極大的影響，尤其在近年越來越受到注目的加密貨幣的領域上，當美國總統候選人唐納・川普（Donald Trump）在競選期間在社交媒體Truth Social積極推廣加密貨幣，卻仍少有研究川普的加密貨幣相關社群貼文對於比特幣市場及價格的關係。以往社群貼文的相關研究⼤多依賴傳統的情緒分析⼯具，這些⼯具難以掌握政治語⾔中的情緒張⼒與修辭複雜性。本研究彌補了這⼀研究缺⼝，運用大型語言模型，深度分析川普社群貼文的情緒及主題，是否與比特幣的市場表現相關。 本研究運⽤⾃然語⾔處理（NLP）技術，分析超過4,000則非結構化貼⽂，並比較傳統情緒分析模型（如 VADER）與基於 transformer 架構的⼤型語⾔模型（如 GPT-3o 與 GPT-4o）之表現，並運用表現最好的模型評估川普加密貨幣敘事的效果。研究樣本取⾃川普在選舉期間發佈的 4,131則 Truth Social 帖⽂，彙整為170筆每⽇情緒觀察值，再與每⽇比特幣報酬率進⾏連結，採⽤具 Newey–West 強健標準誤的普通最⼩平⽅法（OLS）進⾏迴歸分析。情緒分類為正⾯、負⾯與中性，使⽤分類準確率最⾼的 GPT-4o 模型，並控制總體經濟變數、比特幣內部指標與川普的⽀持率。 分析結果顯⽰川普具有⼀致性的發⽂模式，包括常使⽤⺠粹式修辭、反菁英語調與訴諸「⼈⺠」的語⾔風格。雖然⼤多貼⽂情緒偏負⾯，但整體與正向情緒，特別是與加密貨幣相關的內容，與比特幣報酬率的上升呈現顯著正相關。另外，其貼⽂在美國晚間發佈時更易引起受眾關注。GPT-4o 在辨識情緒的表現上優於 GPT-3o 與 VADER，顯⽰其處理政治修辭語境的優勢。本研究也提出⼀個全新的框架，探討加密貨幣敘事如何放⼤情緒訊息對⾦融市場的影響。透過結合情緒分析與敘事建模，超越傳統線性分析途徑，深入揭⽰具情緒渲染與敘事導向的政治訊息如何在⾼波動的去中⼼化市場中形塑投資者⾏為。本研究透過大型語言模型研究社群網路及市場的關係，希望對財務經濟學、政治傳播與⾃然語⾔處理領域皆具學術與實務貢獻，對投資⼈、交易者、政策制定者與學者提供參考價值。
Given the heightened economic uncertainty during the 2024 U.S. presidential election and Donald Trump’s active promotion of cryptocurrency narratives on Truth Social, it is essential to examine how his digital communication influences investor perception and Bitcoin returns. While prior studies have typically relied on traditional sentiment analysis tools that struggle to capture the emotional and rhetorical complexity of political language, this study addresses that gap by analyzing Trump’s posting behavior, key topics, and sentiment. It specifically investigates whether optimism, especially when combined with crypto-related narrativesaffects Bitcoin returns. Utilizing Natural Language Processing (NLP) on more than 4,000 unstructured posts, the study compares the performance of traditional sentiment models such as VADER with transformer-based large language models like GPT-3o and GPT-4o, while also assessing the moderating role of crypto narratives. Drawing from 4,131 Truth Social posts made by Trump during the election period, the content is aggregated into 170 daily sentiment observations and linked to daily Bitcoin returns using OLS regression with Newey–West standard errors Sentiments are classified as positive, negative, or neutral using GPT-4o, the model with the highest classification accuracy, with additional controls for macroeconomic factors, internal Bitcoin indicators, and Trump’s favorability rating. The analysis reveals consistent posting patterns, with Trump frequently employing populist rhetoric, anti-elite framing, and appeals to “the people.” Although his posts were often negatively charged, average and positive sentiments, particularly those referencing cryptocurrency, were significantly associated with increases in Bitcoin returns. Posts that engaged audiences most were typically made during U.S. evening hours. GPT-4o outperformed other models in detecting nuanced populist sentiment, demonstrating its strength over both GPT-3o and VADER. This study introduces a novel moderation framework to explore how crypto-related narratives amplify the effects of Trump’s sentiment on financial markets. By combining sentiment analysis with narrative modeling, it moves beyond linear frameworks to offer a richer understanding of how emotionally framed, narrative-driven messages from influential political figures can shape investor behavior in volatile, decentralized markets. As one of the first to apply GPT-4o in this context, this research contributes to the literature in financial economics, political communication, and NLP, with practical implications for investors, traders, regulators, and scholars.
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描述 碩士
國立政治大學
應用經濟與社會發展英語碩士學位學程(IMES)
112266019
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112266019
資料類型 thesis
dc.contributor.advisor 卞中佩<br>羅秉政zh_TW
dc.contributor.advisor Pien, Chung-Pei<br>Kendro Vincenten_US
dc.contributor.author (Authors) 柯昱安zh_TW
dc.contributor.author (Authors) Ignatius Harry Cahiadharmaen_US
dc.creator (作者) 柯昱安zh_TW
dc.creator (作者) Cahiadharma, Ignatius Harryen_US
dc.date (日期) 2025en_US
dc.date.accessioned 4-Aug-2025 14:18:47 (UTC+8)-
dc.date.available 4-Aug-2025 14:18:47 (UTC+8)-
dc.date.issued (上傳時間) 4-Aug-2025 14:18:47 (UTC+8)-
dc.identifier (Other Identifiers) G0112266019en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/158548-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 應用經濟與社會發展英語碩士學位學程(IMES)zh_TW
dc.description (描述) 112266019zh_TW
dc.description.abstract (摘要) 鑑於2024年美國總統⼤選結果對於美國及全球的經濟將有極大的影響，尤其在近年越來越受到注目的加密貨幣的領域上，當美國總統候選人唐納・川普（Donald Trump）在競選期間在社交媒體Truth Social積極推廣加密貨幣，卻仍少有研究川普的加密貨幣相關社群貼文對於比特幣市場及價格的關係。以往社群貼文的相關研究⼤多依賴傳統的情緒分析⼯具，這些⼯具難以掌握政治語⾔中的情緒張⼒與修辭複雜性。本研究彌補了這⼀研究缺⼝，運用大型語言模型，深度分析川普社群貼文的情緒及主題，是否與比特幣的市場表現相關。 本研究運⽤⾃然語⾔處理（NLP）技術，分析超過4,000則非結構化貼⽂，並比較傳統情緒分析模型（如 VADER）與基於 transformer 架構的⼤型語⾔模型（如 GPT-3o 與 GPT-4o）之表現，並運用表現最好的模型評估川普加密貨幣敘事的效果。研究樣本取⾃川普在選舉期間發佈的 4,131則 Truth Social 帖⽂，彙整為170筆每⽇情緒觀察值，再與每⽇比特幣報酬率進⾏連結，採⽤具 Newey–West 強健標準誤的普通最⼩平⽅法（OLS）進⾏迴歸分析。情緒分類為正⾯、負⾯與中性，使⽤分類準確率最⾼的 GPT-4o 模型，並控制總體經濟變數、比特幣內部指標與川普的⽀持率。 分析結果顯⽰川普具有⼀致性的發⽂模式，包括常使⽤⺠粹式修辭、反菁英語調與訴諸「⼈⺠」的語⾔風格。雖然⼤多貼⽂情緒偏負⾯，但整體與正向情緒，特別是與加密貨幣相關的內容，與比特幣報酬率的上升呈現顯著正相關。另外，其貼⽂在美國晚間發佈時更易引起受眾關注。GPT-4o 在辨識情緒的表現上優於 GPT-3o 與 VADER，顯⽰其處理政治修辭語境的優勢。本研究也提出⼀個全新的框架，探討加密貨幣敘事如何放⼤情緒訊息對⾦融市場的影響。透過結合情緒分析與敘事建模，超越傳統線性分析途徑，深入揭⽰具情緒渲染與敘事導向的政治訊息如何在⾼波動的去中⼼化市場中形塑投資者⾏為。本研究透過大型語言模型研究社群網路及市場的關係，希望對財務經濟學、政治傳播與⾃然語⾔處理領域皆具學術與實務貢獻，對投資⼈、交易者、政策制定者與學者提供參考價值。zh_TW
dc.description.abstract (摘要) Given the heightened economic uncertainty during the 2024 U.S. presidential election and Donald Trump’s active promotion of cryptocurrency narratives on Truth Social, it is essential to examine how his digital communication influences investor perception and Bitcoin returns. While prior studies have typically relied on traditional sentiment analysis tools that struggle to capture the emotional and rhetorical complexity of political language, this study addresses that gap by analyzing Trump’s posting behavior, key topics, and sentiment. It specifically investigates whether optimism, especially when combined with crypto-related narrativesaffects Bitcoin returns. Utilizing Natural Language Processing (NLP) on more than 4,000 unstructured posts, the study compares the performance of traditional sentiment models such as VADER with transformer-based large language models like GPT-3o and GPT-4o, while also assessing the moderating role of crypto narratives. Drawing from 4,131 Truth Social posts made by Trump during the election period, the content is aggregated into 170 daily sentiment observations and linked to daily Bitcoin returns using OLS regression with Newey–West standard errors Sentiments are classified as positive, negative, or neutral using GPT-4o, the model with the highest classification accuracy, with additional controls for macroeconomic factors, internal Bitcoin indicators, and Trump’s favorability rating. The analysis reveals consistent posting patterns, with Trump frequently employing populist rhetoric, anti-elite framing, and appeals to “the people.” Although his posts were often negatively charged, average and positive sentiments, particularly those referencing cryptocurrency, were significantly associated with increases in Bitcoin returns. Posts that engaged audiences most were typically made during U.S. evening hours. GPT-4o outperformed other models in detecting nuanced populist sentiment, demonstrating its strength over both GPT-3o and VADER. This study introduces a novel moderation framework to explore how crypto-related narratives amplify the effects of Trump’s sentiment on financial markets. By combining sentiment analysis with narrative modeling, it moves beyond linear frameworks to offer a richer understanding of how emotionally framed, narrative-driven messages from influential political figures can shape investor behavior in volatile, decentralized markets. As one of the first to apply GPT-4o in this context, this research contributes to the literature in financial economics, political communication, and NLP, with practical implications for investors, traders, regulators, and scholars.en_US
dc.description.tableofcontents ACKNOWLEDGEMENTS iii ABSTRACT iv 摘要 v TABLE OF CONTENTS vi LIST OF FIGURES viii LIST OF TABLES ix CHAPTER 1: INTRODUCTION 1 1.1 Background 1 1.1.1 Bitcoin’s Response to U.S. Inflation: Hedge or Safe Haven? 1 1.1.2 Political and Financial Interplay in the U.S. 2024 Election 2 1.2 Narratives, Sentiment, and Bitcoin Market Reactions 3 1.2.1 Trump’s Narratives and Bitcoin 3 1.2.2 Trump’s Sentiment and Bitcoin 6 1.3 Research Gap 8 1.4 Contribution of the Current Study 9 1.5 Research Objective and Questions 10 CHAPTER 2: LITERATURE REVIEW AND THEORETICAL FRAMEWORK 11 2.1 Theoretical Foundations 11 2.1.1 Behavioral Finance Theory 11 2.1.2 Narrative Economics 11 2.2 Empirical Literature Review 12 2.2.1 Advances in Sentiment Analysis 12 2.2.2 Trump’s Populist Communication Patterns on Social Media 15 2.2.3 Narratives and Bitcoin Price Dynamics 16 2.2.4 Sentiment and Bitcoin Price Dynamics 17 2.3 Hypothesis Development 19 CHAPTER 3: DATA AND ESTIMATION STRATEGY 21 3.1 DATA 21 3.1.1 Truth Social Data 21 3.1.1.1 Data Cleaning and Pre-processing 22 3.1.1.2 Descriptive Statistics of Donald Trump Account 25 3.1.2. Donald Trump’s Narratives and Sentiment 27 3.1.2.1 Sentiment Analysis 30 3.1.2.2 Cryptocurrency-Related Narrative 40 3.1.3 Daily Sentiment 43 3.1.4 Daily Bitcoin Returns 43 3.2 Variable Selection 44 3.3 Estimation Strategy 49 CHAPTER 4: EMPIRICAL RESULTS 52 CHAPTER 5: CONCLUSION 62 5.1 Conclusions 62 5.2 Implications 62 5.3 Limitations and Future Research 63 REFERENCES 64zh_TW
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dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112266019en_US
dc.subject (關鍵詞) 情緒分析zh_TW
dc.subject (關鍵詞) 自然語言處理（NLP)zh_TW
dc.subject (關鍵詞) 大型語言模型（LLMs）zh_TW
dc.subject (關鍵詞) Truth Socialzh_TW
dc.subject (關鍵詞) 比特幣報酬zh_TW
dc.subject (關鍵詞) 政治傳播zh_TW
dc.subject (關鍵詞) 加密貨幣敘事zh_TW
dc.subject (關鍵詞) 時間序列分析zh_TW
dc.subject (關鍵詞) OLS 回歸zh_TW
dc.subject (關鍵詞) Pythonzh_TW
dc.subject (關鍵詞) Rzh_TW
dc.subject (關鍵詞) 大數據分析zh_TW
dc.subject (關鍵詞) Sentiment Analysisen_US
dc.subject (關鍵詞) Natural Language Processing (NLP)en_US
dc.subject (關鍵詞) Large Language Models (LLMs)en_US
dc.subject (關鍵詞) Truth Socialen_US
dc.subject (關鍵詞) Bitcoin Returnsen_US
dc.subject (關鍵詞) Political Communicationen_US
dc.subject (關鍵詞) Cryptocurrency Narrativesen_US
dc.subject (關鍵詞) Time Series Analysisen_US
dc.subject (關鍵詞) OLS Regressionen_US
dc.subject (關鍵詞) Pythonen_US
dc.subject (關鍵詞) Ren_US
dc.subject (關鍵詞) Big Data analysisen_US
dc.title (題名) 基於大型語言模型分析2024年美國總統大選期間川普社群媒體發文與比特幣關係研究zh_TW
dc.title (題名) The Study of the Relationship Between Trump's Social Media Posts and Bitcoin Returns During the 2024 U.S. Presidential Election with A Large Language Model Analysisen_US
dc.type (資料類型) thesisen_US
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