BERT集成學習方法於財經文本探勘之論點驅動的情感分析任務

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Taipei Medical University Institutional Repository > 管理學院 > 大數據科技及管理研究所 > 博碩士論文 > Item 987654321/64313

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題名:	BERT集成學習方法於財經文本探勘之論點驅動的情感分析任務 Ensemble BERT Approach for Argument-Driven Sentiment Analysis in Financial Text Mining Applications
作者:	施辰穎 SY, EUGENE LALIS
貢獻者:	大數據科技及管理研究所碩士班張詠淳
關鍵詞:	Financial NLP、Argumentative Mining、Ensemble Technique、Voting Mechanism、Argument Unit Classification
日期:	2024-06-17
上傳時間:	2024-09-30 14:21:00 (UTC+8)
摘要:	Financial discourse contains complex arguments and sentiments that significantly impact market trends and strategic decisions. This thesis presents a novel ensemble approach to argument mining, advancing the state-of-the-art in argument unit identification for the financial domain. By integrating multiple fine-tuned pre-trained language models through robust voting, the pro-posed ensemble architecture effectively leverages diverse model strengths while mitigating in-dividual biases. Rigorous experimentation on the FinArg dataset demonstrated the ensemble's superiority, with the top configuration achieving a 77.083% Macro F1-score, outperforming the best individual model by 0.659%. Comprehensive analysis revealed insights into how voting techniques impact generalization and the trade-offs between flexibility and robustness. Moreo-ver, this work pioneers integrating argument mining with financial sentiment analysis. Incorpo-rating argument probabilities as features into FinancialBERT enhanced sentiment prediction on the Financial Phrase Bank dataset, with Macro F1-scores reaching 87.818% (FPBall) and 96.919% (FPB100). Strong argument-sentiment correlations open avenues for leveraging argu-mentative structure in sentiment analysis. Contributions include the innovative ensemble archi-tecture, insights into ensemble voting dynamics, and pioneering argument-sentiment integration, enabling sophisticated analytical tools for applications like risk assessment and market fore-casting.
描述:	碩士指導教授：張詠淳口試委員：張詠淳口試委員：陳建錦口試委員：蘇家玉
附註:	論文公開日期：2024-07-11
資料類型:	thesis
顯示於類別:	[大數據科技及管理研究所] 博碩士論文

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