本研究旨在探討企業社會責任（CSR）與環境、社會及公司治理（ESG）指標對企業績效的影響，並透過新聞情緒分析進一步驗證不同產業的新聞情緒是否對公司表現產生影響。在過去的商業環境中，企業主要以獲利為目標。然而，隨著社會責任議題的重要性日益提升，CSR逐漸成為企業策略的重要組成部分，企業不僅需考慮盈利，還需承擔環境保護、資源分配及永續發展等責任。ESG指標提供了評估企業可持續發展表現的框架與重要參考依據。研究顯示，ESG表現與企業財務績效密切相關，特別是在新聞媒體對企業的報導中，ESG相關的新聞情緒對公司股價、市值及品牌聲譽等有顯著影響。透過分析ESG新聞情緒，企業能更好地理解利益相關者的期望，進而提升其社會責任形象及市場競爭力。

本研究根據各產業的ESG投入程度，選取台灣金融服務業、高科技產業及傳統製造業三大產業為此次研究對象，本研究蒐集2021年1月1日至2023年12月31日期間的代表性企業新聞情緒數據及財務指標，探討不同產業的ESG新聞情緒對企業績效的影響。研究將分析新聞報導中所反映的ESG情緒，並進一步評估正面與負面新聞情緒如何影響企業財務表現。同時，以Piotroski F-Score作為衡量企業績效的指標，並運用時間序列模型與自然語言模型來預測不同產業中企業的F-Score結果，進而探討相關變數及情緒分數對企業成長與績效的影響。

以本研究蒐集之情緒、財務指標數據為限制前提下，研究結果顯示，情緒分數與F-Score之間存在弱負相關性，可能因市場情感的滯後效應，使其無法即時反映情緒變化。但導入情緒變數後，時間序列與自然語言模型的預測效能大多數有所提升，且預測結果更加精確，表明情緒變數的加入增強了模型的預測能力。在多變且複雜的財務數據環境中，將情緒變數納入預測模型是有效的策略，能提供更全面的數據分析視角。未來研究中，可進一步探討如何有效運用情緒變數來提升企業績效與預測模型的準確性，做為企業在制定策略時的重要參考依據，以此幫助企業能更全面理解如何將情緒變數融入決策過程，進而在動態市場環境中取得競爭優勢。

This study aims to examine the impact of Corporate Social Responsibility (CSR) and Environmental, Social, and Governance (ESG) indicators on corporate performance. It also seeks to further verify, through news sentiment analysis, whether news sentiment across different industries affects company performance. In the past business environment, companies primarily focused on profit. However, as the importance of social responsibility issues has grown, CSR has gradually become an essential part of corporate strategy. Companies are now required to consider not only profitability but also responsibilities such as environmental protection, resource allocation, and sustainable development. ESG indicators provide a framework and essential reference for evaluating a company's sustainable development performance. Research indicates that ESG performance is closely related to corporate financial performance, especially in media coverage, where ESG-related news sentiment significantly impacts stock prices, market capitalization, and brand reputation. By analyzing ESG news sentiment, companies can better understand stakeholder expectations and enhance their social responsibility image and market competitiveness.

Based on the level of ESG investment in each industry, this study selects three major industries in Taiwan—financial services, high-tech, and traditional manufacturing—as the research subjects. The study collects representative corporate news sentiment data and financial indicators from January 1, 2021, to December 31, 2023, to investigate the impact of ESG news sentiment on corporate performance across different industries. The study will analyze the ESG sentiment reflected in news reports and further assess how positive and negative news sentiment influences corporate financial performance. The Piotroski F-Score is used as a metric for measuring corporate performance. Additionally, time series models and natural language models are employed to predict the F-Score results of companies within different industries, exploring the impact of related variables and sentiment scores on corporate growth and performance.

Within the constraints of the sentiment and financial indicator data collected in this study, the results show a weak negative correlation between sentiment scores and F-Score. This may be due to the lag effect of market sentiment, which prevents it from reflecting sentiment changes immediately. However, after introducing sentiment variables, the predictive performance of most time series and natural language models improved, and the predictions became more accurate. This indicates that the inclusion of sentiment variables enhanced the models' predictive capabilities. In a complex and dynamic financial data environment, incorporating sentiment variables into predictive models is an effective strategy that provides a more comprehensive perspective on data analysis. Future research could further explore how to effectively use sentiment variables to improve corporate performance and the accuracy of predictive models, serving as a valuable reference for companies when formulating strategies. This can help businesses better understand how to integrate sentiment variables into the decision-making process, thereby gaining a competitive advantage in a dynamic market environment.

論文審定書 i
誌謝 ii
摘要 iii
Abstract iv
目 錄 v
圖目錄 vii
表目錄 viii
第一章、 緒論 1
第一節、 研究背景 1
第二節、 研究動機與目的 3
第二章、 文獻探討 5
第一節、 企業社會責任 5
一、 CSR對公司的影響 5
二、 ESG對公司的影響 5
第二節、 新聞媒體情緒對公司績效影響 7
第三節、 MSCI - ESG評估指標 8
第四節、 時間序列分析 12
一、 自迴歸整合移動平均模型(ARIMA) 12
二、 自迴歸（VAR）模型 13
第五節、 文本處理與情感分析模型方法 15
一、 中文斷詞處理-Jieba 15
二、 TF-IDF(詞頻與逆向逆向文件頻率) 16
三、 簡單循環神經網路（SimpleRNN） 17
四、 長短期記憶神經網路(LSTM) 18
五、 雙向長短期記憶網絡(BiLSTM) 19
六、 閘門循環單元(GRU) 21
七、 BERT相關模型 24
八、 LDA模型 28
第六節、 衡量公司財務績效指標 - Piotroski F-Score 30
第三章、 研究方法 33
第一節、 研究流程架構 33
第二節、 資料來源與蒐集 35
第三節、 資料預處理與分類方法 36
第四節、 變數設定 37
第五節、 模型變數驗證 44
第六節、 實證模型建立 46
第七節、 模型效能評估 48
第四章、 研究實證成果 51
第一節、 敘述統計分析 51
一、 一般資料敘述統計 51
二、 相關性分析 53
三、 迴歸分析 67
第二節、 時間序列模型與自然語言模型分析 75
一、 平穩性檢定 75
二、 預測模型建立方法 77
三、 時間序列模型與自然語言模型實證結果 80
第五章、 結論 94
第一節、 研究結論 94
第二節、 研究限制與建議 97
參考文獻 99
附錄 111

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