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研究生:陳重吉
研究生(外文):Chung-Chi Chen
論文名稱:深度財務意見探勘
論文名稱(外文):Fine-grained Financial Opinion Mining
指導教授:陳信希陳信希引用關係
指導教授(外文):Hsin-Hsi Chen
口試委員:鄭卜壬陳建錦魏志平王釧茹吳宗憲曾新穆蔡宗翰禹良治
口試委員(外文):Pu-Jen ChengChien-Chin ChenChih-Ping WeiChuan-Ju WangChung-Hsien WuShin-Mu TsengTzong-Han TsaiLiang-Chih Yu
口試日期:2021-01-19
學位類別:博士
校院名稱:國立臺灣大學
系所名稱:資訊工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2021
畢業學年度:109
語文別:英文
論文頁數:147
中文關鍵詞:意見探勘自然語言處理財務意見
外文關鍵詞:Opinion MiningFinancial Opinion MiningNatural Language Processing
DOI:10.6342/NTU202100175
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 意見探勘一直以來都是自然語言處理領域中,非常熱門的話題之一,然而,在財務意見探勘上,多數研究僅止於情緒分析,而未進一步的探討財務意見的細節。在本研究中,我們深入探討財務意見的要點,並著重於投資人意見探勘的研究,除了對當前的相關研究進行調查,亦列出12項探討投資人意見的重要項目,並針對投資人意見的特性設計創新的任務、標記專家資料集以及建立神經網路模型進行深度投資人意見探勘。
  金融市場中的資訊可以分為四種,公司發布的官方資訊、新聞發布的事件資訊、分析師發布的分析結果以及財務社群平台上群眾的討論,本研究於財務社群上進行多項深度意見探勘的研究,包括市場情緒分析、意見依據、數字理解、數字依附、意見品質等研究議題,並延伸前述項目的研究成果到其他三種正式文件上,除提升意見探勘的準確度外,亦進行跨文件的推論及分析。
  為了深入探索財務意見內容,本研究發布五份專家標記的資料集及兩份語料,基於統計分析及機器學習模型的實驗結果,本研究發現(1)市場情緒和一般情緒不同,兩者應分開進行判斷及分析;(2)數字資訊在財務文本中所佔的比例較其他文本高,且理解數字資訊可以提升模型理解財務文本的能力;(3)將數字資訊以特殊的方式編碼,可以提升模型對數字資訊的理解能力;(4)透過與分析師報告中的分析依據比較,我們可以找出高品質的群眾意見:(5)依本研究方法所取出的群眾意見,於獲利及風險兩個面向均可與專業分析師媲美。
  本研究亦將研究成果發展為展示系統,提供實時的資訊供投資人參考,並將深度意見探勘的研究成果延伸到財務領域的應用,包含財務社群媒體個人化推薦系統、優化傳統以關鍵字為基礎的經濟政策不確定性指標以及提升於公司法說會中Non-GAAP項目擷取的準確度。
  Opinion mining is one of the most popular topics in the Natural Language Processing field for a long time. However, most of the previous works on financial opinion mining still only pay their attention to sentiment analysis, but not take a close look at the fine-grained information embedding in a financial opinion. In this thesis, we provide a research agenda for the fine-grained financial opinion mining and list twelve components in a financial opinion. We probe several novel challenges in fine-grained financial opinion mining, provide the related expert-annotated datasets, and propose some insights for constructing the neural network models for these tasks.   
  In the financial market, the information sources can be separated into four kinds: (1) the official information published by companies, (2) the events mentioned in the news articles, (3) the analysis reports, and (4) the discussion on financial social media platforms. We first explore the issues in financial social media data, including market sentiment analysis, aspect analysis, numeral understanding task, numeral attachment task, and opinion quality issue, then extend the results to other information sources. Besides, we also provide the experiments and comparisons on the cross sources inference issue.  
  For probing financial opinion mining, we propose five datasets and two corpora, including FinNum, NumAttach, NumClaim, MultiNum, Numeracy-600K, NTUSD-Fin, and Fin-SoMe. Based on our experimental results and statistics, we have the following findings. First, market sentiment is different from the general sentiment. That means we need to consider the writer’s sentiment and the writer’s market sentiment separately. Second, the proportion of numerals in financial documents is higher than that in the documents in other domains. Understanding the meaning of the numerals is helpful for understanding the financial documents. Third, independently encoding the numeral information is needed and it is helpful for improving the performance of neural network models on numeral understanding tasks. Fourth, we can mine the high-quality opinions from the crowd by comparing their rationales with professional analysts. Last, from both profitability and risk control aspects, the crowd opinions extracted by the proposed methods are comparable, even better, than the professional analysts’ opinions.
  We further construct the demonstration systems for real-world usage and apply our results to the issues in the financial domain. For example, we propose a personal recommendation system for users on financial social media platforms. We also improve the explainability and the predictability of the economic policy index and propose a method to extract the Non-GAAP metrics in earning conference call.
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Thesis Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Financial Opinion Definition 5
2.1 Coarse-grained Financial Opinion . . . . . . . . . . . . . . . . . . . . . 5
2.2 Fine-grained Financial Opinion . . . . . . . . . . . . . . . . . . . . . . . 7
2.3 Argument Mining in Finance . . . . . . . . . . . . . . . . . . . . . . . . 9
2.4 Quality Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.5 Inferring Implicit Influence . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.6 Retrieval and Summarization . . . . . . . . . . . . . . . . . . . . . . . . 12
2.7 Tracing in Time Series . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3 Market Sentiment and Aspect Analysis 15
3.1 Expert-annotated Financial Social Media Data . . . . . . . . . . . . . . . 15
3.1.1 Statistics of the Collected Dataset . . . . . . . . . . . . . . . . . 17
3.1.2 Annotation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.1.3 Market Sentiment and Writer Sentiment . . . . . . . . . . . . . . 18
3.1.4 Founded/Unfounded and Gain/Loss . . . . . . . . . . . . . . . . 19
3.1.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.2 Market Sentiment Dictionary . . . . . . . . . . . . . . . . . . . . . . . . 21
3.2.1 Different Methods for Constructing Dictionary . . . . . . . . . . 21
3.2.2 Different between General Sentiment and Market Sentiment . . . 22
3.3 Key Snippet Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.3.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.3.2 Approaches to Key Snippet Extraction . . . . . . . . . . . . . . . 24
3.3.3 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.3.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . 26
3.4 Aspect Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.4.1 Aspects of Investor’s Opinion . . . . . . . . . . . . . . . . . . . 26
3.4.2 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . 28
3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4 Numeral Understanding 31
4.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.3 Numeral Taxonomy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.3.1 Monetary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.3.2 Percentage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.3.3 Option . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.3.4 Indicator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.3.5 Temporal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.3.6 Quantity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.3.7 Product/Version Number . . . . . . . . . . . . . . . . . . . . . . 39
4.4 Data Annotation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.4.1 Inter-Annotator Agreement . . . . . . . . . . . . . . . . . . . . . 40
4.4.2 Annotation Results . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.5 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.5.1 Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.5.2 Word Vector . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.5.3 Support Vector Mechine (SVM) . . . . . . . . . . . . . . . . . . 43
4.5.4 Convolutional Neural Network (CNN) . . . . . . . . . . . . . . . 43
4.5.5 Recurrent Neural Network (RNN) . . . . . . . . . . . . . . . . . 45
4.6 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.6.1 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.6.2 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.6.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . 46
4.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.7.1 Model Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.7.2 Error Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.8 Numeral Attachment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.8.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.8.2 Overall Performance . . . . . . . . . . . . . . . . . . . . . . . . 54
4.8.3 Ablation Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.9 Crowd-Based Trading Model . . . . . . . . . . . . . . . . . . . . . . . . 56
4.9.1 Crowd vs. Analyst Opinions . . . . . . . . . . . . . . . . . . . . 57
4.9.2 Trading Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.10 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
5 Argument Mining 61
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
5.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
5.3 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
5.3.1 Dataset Construction . . . . . . . . . . . . . . . . . . . . . . . . 64
5.3.2 Comparison between Datasets . . . . . . . . . . . . . . . . . . . 65
5.4 Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
5.4.1 Vanilla Neural Network Architecture . . . . . . . . . . . . . . . 66
5.4.2 Class Weight . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
5.4.3 Numeral Encoder . . . . . . . . . . . . . . . . . . . . . . . . . . 67
5.4.4 Joint Learning with Category Classification Task . . . . . . . . . 67
5.5 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
5.5.1 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . 68
5.5.2 Discussion of Numeral Encoder . . . . . . . . . . . . . . . . . . 69
5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
6 Quality Evaluation 71
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
6.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
6.3 Task Setting and Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . 75
6.3.1 Rationale of Task Setting . . . . . . . . . . . . . . . . . . . . . . 75
6.3.2 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
6.4 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
6.4.1 Discriminating Expert Rationales . . . . . . . . . . . . . . . . . 77
6.4.2 Mining High-quality Opinions . . . . . . . . . . . . . . . . . . . 79
6.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
6.5.1 Comparison with Analysts . . . . . . . . . . . . . . . . . . . . . 81
6.5.2 Comparison between Different Ranking Methods . . . . . . . . . 83
6.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
7 Exaggerated Information Detection 87
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
7.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
7.3 Task Setting and Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . 90
7.4 Empirical Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
7.4.1 Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
7.4.2 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . 93
7.4.3 Error Analysis and Future Research . . . . . . . . . . . . . . . . 94
7.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
7.5.1 Exaggerated Numeral Detection . . . . . . . . . . . . . . . . . . 95
7.5.2 Numeracy in Open-Domain Article Titles . . . . . . . . . . . . . 96
7.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
8 Opinion Retrieval 99
8.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
8.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
8.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
8.4 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
8.5 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
8.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
8.6.1 Influence of Input Length . . . . . . . . . . . . . . . . . . . . . . 105
8.6.2 Ablation Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 107
8.6.3 Error Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
8.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
9 FinTech Applications 109
9.1 Know Your Customer (KYC) . . . . . . . . . . . . . . . . . . . . . . . . 109
9.1.1 Corporate Customers . . . . . . . . . . . . . . . . . . . . . . . . 109
9.1.2 Personal Customers . . . . . . . . . . . . . . . . . . . . . . . . . 110
9.1.3 Future Research Directions . . . . . . . . . . . . . . . . . . . . . 111
9.1.4 Personalized Recommendation System for Investor’s: Next Cashtag Prediction on Social Trading Platforms . . . . . . . . . . . . 111
9.2 Know Your Product (KYP) . . . . . . . . . . . . . . . . . . . . . . . . . 114
9.2.1 Prospect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
9.2.2 Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
9.2.3 Opportunities . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
9.2.4 Crowd View: Converting Investors’ Opinions into Indicators . . . 116
9.3 Satisfy Your Customer (SYC) . . . . . . . . . . . . . . . . . . . . . . . 121
9.3.1 Business to Customer (B2C) . . . . . . . . . . . . . . . . . . . . 122
9.3.2 Customer to Customer (C2C) . . . . . . . . . . . . . . . . . . . 122
9.3.3 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
9.3.4 Find the Experts in the Crowd: Recommending Expert-like Opinion to Investors . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
9.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
10 Conclusion 125
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