臺灣博碩士論文加值系統

本研究的目標在於解決預測企業永續治理表現問題，解決此問題有助於企業評估自身永續治理表現以及協助利害關係人獲取重要資訊。我們建立了一個全新的員工評論文字資料集，透過文字探勘方法萃取員工評論當中的資訊來提升模型預測能力。同時我們設計並實現了一個有效、預測能力高於現有 ESG 自動評分方法的自動評分系統，並且透過進行了一系列實驗來對包括 SVM、Multilayer Perceptron、BERT、RoBERTa、ESG-BERT 等不同機器學習模型與深度神經網路進行評估，找出表現最佳的模型。實驗部份應用到的相關理論和文獻包括機器學習、深度學習、文字探勘。我們使用自行建立的的數據集在我們所提出的模型和方法在上進行了交叉驗證的性能評估。實驗結果證明了我們的方法在 ESG 評分預測方面實現了良好的準確率，並超越了先前相關研究在這個領域的研究成果。

This study aims to address the challenge of predicting ESG performance using employee review text data. We establish a novel employee review text dataset and employ text mining techniques to extract information from employee reviews to enhance the predictive power of our models. Additionally, we design and implement an automated scoring system that outperforms existing ESG rating systems. We evaluate various machine learning models and deep neural networks, including SVM, Multilayer Perceptron, BERT, RoBERTa, and DistilBERT, using a series of experiments to identify the optimal model. The experiments are grounded in relevant theories and literature from machine learning, deep learning, and text mining. We conduct cross-validation performance evaluations on our proposed models and methods using our self-constructed dataset. The experimental results demonstrate the effectiveness of our approach in achieving high accuracy for ESG scoring and surpass the state-of-the-art results in this domain.

論文審定書i
摘要ii
Abstractiii
目錄iv
圖次vii
表次viii
第一章 緒論1
第二章 文獻探討6
2.1 ESG 如何為企業創造價值6
2.2 ESG 評級相關研究文獻8
2.3員工與企業永續治理之關聯9
2.4過去相關研究方法11
2.4.1 採用機器學習方法預測 ESG 評級13
2.4.2 採用文字探勘方法預測 ESG 評級17
第三章 研究方法24
3.1研究問題定義24
3.2研究方法架構25
3.2.1員工評論25
3.2.2 企業財務比率26
3.3研究方法總結26
第四章 實驗設計28
4.1資料集蒐集與敘述28
4.2實驗設置31
4.3比較方法之實驗設置33
4.3.1機器學習方法33
4.3.2文字探勘方法33
4.4評估方法34
第五章 實驗結果35
5.1以文字評論與財務數據做為輸入35
5.2與機器學習方法之比較39
5.3與文字探勘方法之比較43
5.4額外實驗45
5.5實驗結果討論47
第六章 結論49
附錄一51
附錄二55
附錄三56
參考文獻57

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