From Opinion Mining to Financial Argument Mining

From Opinion Mining to Financial Argument Mining
Author :
Publisher : Springer Nature
Total Pages : 102
Release :
ISBN-10 : 9789811628818
ISBN-13 : 9811628815
Rating : 4/5 (815 Downloads)

Book Synopsis From Opinion Mining to Financial Argument Mining by : Chung-Chi Chen

Download or read book From Opinion Mining to Financial Argument Mining written by Chung-Chi Chen and published by Springer Nature. This book was released on 2021 with total page 102 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.


From Opinion Mining to Financial Argument Mining Related Books

From Opinion Mining to Financial Argument Mining
Language: en
Pages: 102
Authors: Chung-Chi Chen
Categories: Application software
Type: BOOK - Published: 2021 - Publisher: Springer Nature

DOWNLOAD EBOOK

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
Advanced Technologies, Systems, and Applications VIII
Language: en
Pages: 631
Authors: Naida Ademović
Categories: Technology & Engineering
Type: BOOK - Published: 2023-10-02 - Publisher: Springer Nature

DOWNLOAD EBOOK

This book presents proceedings of the 14th Days of Bosnian-Herzegovinian American Academy of Arts and Sciences held in Tuzla, BIH, June 1–4, 2023. Delve into
Beyond Fintech
Language: en
Pages: 282
Authors: Bernardo Nicoletti
Categories: Business & Economics
Type: BOOK - Published: 2022-04-11 - Publisher: Springer Nature

DOWNLOAD EBOOK

Enterprise management theories about the so-called bionic organization currently face a significant funding gap. Bionic theories have been mainly applied to ent
Data Mining in Finance
Language: en
Pages: 323
Authors: Boris Kovalerchuk
Categories: Computers
Type: BOOK - Published: 2005-12-11 - Publisher: Springer Science & Business Media

DOWNLOAD EBOOK

Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, rule
Data Preparation for Data Mining
Language: en
Pages: 566
Authors: Dorian Pyle
Categories: Computers
Type: BOOK - Published: 1999-03-22 - Publisher: Morgan Kaufmann

DOWNLOAD EBOOK

This book focuses on the importance of clean, well-structured data as the first step to successful data mining. It shows how data should be prepared prior to mi