From Opinion Mining to Financial Argument Mining
Opinion mining is a prevalent research issue in many domains. In the financial domain, however, it is still in the early stages. Most of the researches on this topic only focus on the coarse-grained market sentiment analysis, i.e., 2-way classification for bullish/bearish. Thanks to the recent finan...
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| Médium: | E-kniha |
| Jazyk: | angličtina |
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Singapore
Springer Nature
2021
Springer Ministry of Science and Technology of Taiwan |
| Vydání: | 1 |
| Edice: | SpringerBriefs in Computer Science |
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| ISBN: | 9789811628818, 9811628815, 9789811628801, 9811628807 |
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| Abstract | Opinion mining is a prevalent research issue in many domains. In the financial domain, however, it is still in the early stages. Most of the researches on this topic only focus on the coarse-grained market sentiment analysis, i.e., 2-way classification for bullish/bearish. Thanks to the recent financial technology (FinTech) development, some interdisciplinary researchers start to involve in the in-depth analysis of investors' opinions. These works indicate the trend toward fine-grained opinion mining in the financial domain. When expressing opinions in finance, terms like bullish/bearish often spring to mind. However, the market sentiment of the financial instrument is just one type of opinion in the financial industry. Like other industries such as manufacturing and textiles, the financial industry also has a large number of products. Financial services are also a major business for many financial companies, especially in the context of the recent FinTech trend. For instance, many commercial banks focus on loans and credit cards. Although there are a variety of issues that could be explored in the financial domain, most researchers in the AI and NLP communities only focus on the market sentiment of the stock or foreign exchange. This open access book addresses several research issues that can broaden the research topics in the AI community. It also provides an overview of the status quo in fine-grained financial opinion mining to offer insights into the futures goals. For a better understanding of the past and the current research, it also discusses the components of financial opinions one-by-one with the related works and highlights some possible research avenues, providing a research agenda with both micro- and macro-views toward financial opinions. |
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| AbstractList | Opinion mining is a prevalent research issue in many domains. In the financial domain, however, it is still in the early stages. Most of the researches on this topic only focus on the coarse-grained market sentiment analysis, i.e., 2-way classification for bullish/bearish. Thanks to the recent financial technology (FinTech) development, some interdisciplinary researchers start to involve in the in-depth analysis of investors' opinions. These works indicate the trend toward fine-grained opinion mining in the financial domain. When expressing opinions in finance, terms like bullish/bearish often spring to mind. However, the market sentiment of the financial instrument is just one type of opinion in the financial industry. Like other industries such as manufacturing and textiles, the financial industry also has a large number of products. Financial services are also a major business for many financial companies, especially in the context of the recent FinTech trend. For instance, many commercial banks focus on loans and credit cards. Although there are a variety of issues that could be explored in the financial domain, most researchers in the AI and NLP communities only focus on the market sentiment of the stock or foreign exchange. This open access book addresses several research issues that can broaden the research topics in the AI community. It also provides an overview of the status quo in fine-grained financial opinion mining to offer insights into the futures goals. For a better understanding of the past and the current research, it also discusses the components of financial opinions one-by-one with the related works and highlights some possible research avenues, providing a research agenda with both micro- and macro-views toward financial opinions. |
| Author | Chen, Chung-Chi Chen, Hsin-Hsi Huang, Hen-Hsen |
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| Snippet | Opinion mining is a prevalent research issue in many domains. In the financial domain, however, it is still in the early stages. Most of the researches on this... |
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| SubjectTerms | Algorithms & data structures Algorithms and data structures argument mining in finance Artificial intelligence Computer and Information Systems Applications Computer Applications Computer programming / software engineering Computer science Computer Technology Computing and Information Technology Data mining Data Mining and Knowledge Discovery Data Science Data Structures and Information Theory Expert systems / knowledge-based systems financial opinion mining financial technology application FinTech Information technology: general issues Information technology: general topics Information theory Natural language & machine translation Natural language and machine translation Natural Language Processing (NLP) Nonfiction numeral understanding Open Access opinion quality evaluation text mining in finance |
| SubjectTermsDisplay | Computer Technology. Electronic books. Nonfiction. |
| TableOfContents | Intro -- Preface -- Contents -- 1 Introduction -- 1.1 Opinion Mining and Sentiment Analysis -- 1.2 Financial Opinion Mining -- 1.3 Why Study Financial Opinion Mining? -- 1.4 Overview of the Book -- References -- 2 Modeling Financial Opinions -- 2.1 Opinion Components -- 2.1.1 Target Entity -- 2.1.2 Market Sentiment -- 2.1.3 Opinion Holder -- 2.1.4 Publishing Time and Validity Period -- 2.1.5 Market Information -- 2.1.6 Aspect -- 2.1.7 Elementary Argumentative Units -- 2.1.8 Opinion Quality -- 2.1.9 Influence -- 2.2 Argumentation Structure in Opinions -- 2.3 Argumentation Structure Among Opinions -- 2.4 Relations Among Opinions and Target Entities -- 2.5 Summary -- References -- 3 Sources and Corpora -- 3.1 Insiders -- 3.2 Professionals -- 3.3 Social Media Users -- 3.4 Journalists -- 3.5 Summary -- References -- 4 Organizing Financial Opinions -- 4.1 Component Extraction -- 4.1.1 Target Entity and Opinion Holder -- 4.1.2 Market Sentiment and Aspect -- 4.1.3 Temporal Information -- 4.1.4 Elementary Argumentative Units -- 4.2 Relation Linking and Quality Evaluation -- 4.3 Influence Power Estimation and Implicit Information Inference -- 4.4 Summary -- References -- 5 Numerals in Financial Narratives -- 5.1 Numeral Understanding -- 5.2 Numeral Attachment -- 5.3 Improving Financial Opinion Mining via Numeral-Related Tasks -- 5.4 Summary -- References -- 6 FinTech Applications -- 6.1 Information Provision -- 6.2 Personalized Recommendation -- 6.3 Improving Employee Efficiency -- 6.4 Summary -- References -- 7 Perspectives and Conclusion -- 7.1 Future Directions -- 7.2 Conclusion -- References |
| Title | From Opinion Mining to Financial Argument Mining |
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