企業社會責任（CSR）近年來引起了廣泛關注，其對企業財務績效（CFP）的影響一直存在著持續的討論。然而，過去在這一領域的研究主要集中在由評鑑機構所評估的CSR表現上，而不是CSR報告書內容的揭露。本研究採用文字探勘的方式來衡量永續報告書中環境和社會揭露，並客觀的研究環境面揭露的深度、廣度和社會面利害關係人的揭露對公司財務績效的影響。此外，我們提出了一種使用無標籤資料進行自動主體種子詞挑選的方式，並應用在GuidedLDA主題模型上，該方法可以引導主題模型判斷出在CSR報告書中與全球報告倡議組織（GRI）標準一致的主題。我們蒐集臺灣上市公司的CSR報告以及臺灣金融資料庫TEJ中2016年至2020年間的資料進行分析。結果顯示，CSR在環境上的深度及廣度的揭露對公司的CFP有顯著影響，而不同的利害關係人的揭露也會對不同的財務指標有所影響。我們期望我們的研究結果能夠幫助企業更妥善的規劃CSR揭露的策略，以增加其在CSR未來活動的價值，此外，也能幫助投資者更加客觀的衡量企業的CSR揭露，而不只是依賴評鑑機構給予的分數。

Corporate social responsibility (CSR) has gained much attention in recent years, and its impact on corporate financial performance (CFP) is in constant debate. However, previous studies in this line of research have primarily focused on CSR performance evaluated by rating agencies rather than the disclosure of CSR reports. This thesis adopts a text-mining approach to measure environmental and social disclosure and examine the effects of depth, breadth, and stakeholders of disclosure on firm performance objectively. Additionally, we propose a novel approach for automatic seed word selection without label data in GuidedLDA, which can guide the topic to align the topics in CSR reports with the Global Reporting Initiative (GRI) Standards. We retrieve the CSR reports of listed companies in Taiwan and their financial data from TEJ, the prevalent financial database in Taiwan, between 2016 to 2020. Our findings show that the level of detail and coverage in CSR disclosure significantly affect a company’s CFP. We expect our findings to help companies in strategically planning their CSR disclosure, thereby adding value to their future activities. Moreover, our research aims to enable investors to objectively measure firms' CSR disclosure without solely relying on rating agencies.

論文審定書i
致謝ii
摘要iii
ABSTRACTiv
TABLE OF CONTENTv
TABLE OF FIGUREvii
TABLE OF TABLEviii
1 INTRODUCTION1
2 RELATED WORKS6
2.1 CSR AND FIRM PERFORMANCE6
2.1.1 CSR Performance and Firm Performance7
2.1.2 CSR performance and CSR disclosure8
2.1.3 CSR disclosure and Firm performance9
2.2 CSR AND TEXT MINING10
3 CSR DISCLOSURE CONSTRUCTION12
3.1 ENVIRONMENTAL DISCLOSURE MEASUREMENT12
3.1.1 Breadth Score13
3.1.2 Depth Score15
3.2 SOCIAL DISCLOSURE MEASUREMENT16
4 METHODOLOGY17
4.1 TOPIC MODEL18
4.2 AUTOMATIC SEED WORDS SELECTION METHOD FOR GUIDEDLDA19
4.2 NAME ENTITY RECOGNITION MODEL21
4.3 REGRESSIONS MODEL22
5 EXPERIMENTS23
5.1 DATA PREPARATION23
5.2 AUTOMATIC SEED WORDS SELECTION METHOD FOR GUIDEDLDA27
5.2.1 Evaluation Setting27
5.2.2 Experiment Results on Automatic Seed Words Selection28
5.2.3 Result on Automatic Seed Words Selection Method for GuidedLDA31
5.2.3 Topic Model Settings34
5.4 RESULT ON REGRESSION MODEL35
6 CONCLUSION39
REFERENCES41

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