From Opinion Mining to Financial Argument Mining

From Opinion Mining to Financial Argument Mining

Author: Chung-Chi Chen

Publisher: Springer Nature

ISBN: 9789811628818

Category: Application software

Page: 95

View: 629

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.

Computer and Information Science 2021—Summer

Computer and Information Science 2021—Summer

Author: Roger Lee

Publisher: Springer Nature

ISBN: 9783030794743

Category: Technology & Engineering

Page: 202

View: 674

This edited book presents scientific results of the 20th IEEE/ACIS International Summer Semi-Virtual Conference on Computer and Information Science (ICIS 2021) held on June 23–25, 2021 in Shanghai, China. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. Research results about all aspects (theory, applications and tools) of computer and information science, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them. The conference organizers selected the best papers from those papers accepted for presentation at the conference. The papers were chosen based on review scores submitted by members of the program committee and underwent further rigorous rounds of review. From this second round of review, 13 of the conference’s most promising papers are then published in this Springer (SCI) book and not the conference proceedings. We impatiently await the important contributions that we know these authors will bring to the field of computer and information science.

Argument Mining

Argument Mining

Author: Mathilde Janier

Publisher: John Wiley & Sons

ISBN: 9781119671169

Category: Computers

Page: 202

View: 101

This book is an introduction to the linguistic concepts of argumentation relevant for argument mining, an important research and development activity which can be viewed as a highly complex form of information retrieval, requiring high-level natural language processing technology. While the first four chapters develop the linguistic and conceptual aspects of argument expression, the last four are devoted to their application to argument mining. These chapters investigate the facets of argument annotation, as well as argument mining system architectures and evaluation. How annotations may be used to develop linguistic data and how to train learning algorithms is outlined. A simple implementation is then proposed. The book ends with an analysis of non-verbal argumentative discourse. Argument Mining is an introductory book for engineers or students of linguistics, artificial intelligence and natural language processing. Most, if not all, the concepts of argumentation crucial for argument mining are carefully introduced and illustrated in a simple manner.

Natural Language Processing and Information Systems

Natural Language Processing and Information Systems

Author: Elisabeth Métais

Publisher: Springer Nature

ISBN: 9783030805999

Category: Computers

Page: 386

View: 302

This book constitutes the refereed proceedings of the 26th International Conference on Applications of Natural Language to Information Systems, NLDB 2021, held online in July 2021. The 19 full papers and 14 short papers were carefully reviewed and selected from 82 submissions. The papers are organized in the following topical sections: role of learning; methodological approaches; semantic relations; classification; sentiment analysis; social media; linking documents; multimodality; applications.

Neural Information Processing

Neural Information Processing

Author: Long Cheng

Publisher: Springer

ISBN: 9783030042219

Category: Computers

Page: 573

View: 207

The seven-volume set of LNCS 11301-11307 constitutes the proceedings of the 25th International Conference on Neural Information Processing, ICONIP 2018, held in Siem Reap, Cambodia, in December 2018. The 401 full papers presented were carefully reviewed and selected from 575 submissions. The papers address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniques across different domains. The 5th volume, LNCS 11305, is organized in topical sections on prediction; pattern recognition; and word, text and document processing.

Inference in Argumentation

Inference in Argumentation

Author: Eddo Rigotti

Publisher: Springer

ISBN: 9783030045685

Category: Language Arts & Disciplines

Page: 325

View: 306

This book investigates the role of inference in argumentation, considering how arguments support standpoints on the basis of different loci. The authors propose and illustrate a model for the analysis of the standpoint-argument connection, called Argumentum Model of Topics (AMT). A prominent feature of the AMT is that it distinguishes, within each and every single argumentation, between an inferential-procedural component, on which the reasoning process is based; and a material-contextual component, which anchors the argument in the interlocutors’ cultural and factual common ground. The AMT explains how these components differ and how they are intertwined within each single argument. This model is introduced in Part II of the book, following a careful reconstruction of the enormously rich tradition of studies on inference in argumentation, from the antiquity to contemporary authors, without neglecting medieval and post-medieval contributions. The AMT is a contemporary model grounded in a dialogue with such tradition, whose crucial aspects are illuminated in this book.

Innovations, Algorithms, and Applications in Cognitive Informatics and Natural Intelligence

Innovations, Algorithms, and Applications in Cognitive Informatics and Natural Intelligence

Author: Chui, Kwok Tai

Publisher: IGI Global

ISBN: 9781799830405

Category: Science

Page: 403

View: 829

While cognitive informatics and natural intelligence are receiving greater attention by researchers, multidisciplinary approaches still struggle with fundamental problems involving psychology and neurobiological processes of the brain. Examining the difficulties of certain approaches using the tools already available is vital for propelling knowledge forward and making further strides. Innovations, Algorithms, and Applications in Cognitive Informatics and Natural Intelligence is a collection of innovative research that examines the enhancement of human cognitive performance using emerging technologies. Featuring research on topics such as parallel computing, neuroscience, and signal processing, this book is ideally designed for engineers, computer scientists, programmers, academicians, researchers, and students.

Encyclopedia of Business Ethics and Society

Encyclopedia of Business Ethics and Society

Author: Robert W. Kolb

Publisher: SAGE

ISBN: 9781412916523

Category: Business & Economics

Page: 2437

View: 428

The five volumes of this ultimate resource recognize the inherent unity between business ethics and business and society, that stems from their shared primary concern with value in commerce. This Encyclopedia spans the relationships among business, ethics, and society by including more than 800 entries that feature broad coverage of corporate social responsibility, the obligation of companies to various stakeholder groups, the contribution of business to society and culture, and the relationship between organizations and the quality of the environment.

Enterprise Applications and Services in the Finance Industry

Enterprise Applications and Services in the Finance Industry

Author: Fethi A. Rabhi

Publisher: Springer

ISBN: 9783642362194

Category: Business & Economics

Page: 131

View: 660

This book constitutes the proceedings of the 6th International Workshop on Enterprise Applications and Services in the Finance Industry, FinanceCom 2012, held in Barcelona, Spain, on June 10, 2012. The workshop spans multiple disciplines, including technical, service, economic, sociological, and behavioral sciences. It reflects on technologically enabled opportunities, implications, and changes due to the introduction of new business models or regulations related to the financial services industry and the financial markets. The seven papers presented were carefully reviewed and selected from numerous submissions. The topics covered are: news and text analysis; algorithmic and high-frequency trading; and the role and impact of technology.

R: Mining spatial, text, web, and social media data

R: Mining spatial, text, web, and social media data

Author: Bater Makhabel

Publisher: Packt Publishing Ltd

ISBN: 9781788290814

Category: Computers

Page: 651

View: 725

Create data mining algorithms About This Book Develop a strong strategy to solve predictive modeling problems using the most popular data mining algorithms Real-world case studies will take you from novice to intermediate to apply data mining techniques Deploy cutting-edge sentiment analysis techniques to real-world social media data using R Who This Book Is For This Learning Path is for R developers who are looking to making a career in data analysis or data mining. Those who come across data mining problems of different complexities from web, text, numerical, political, and social media domains will find all information in this single learning path. What You Will Learn Discover how to manipulate data in R Get to know top classification algorithms written in R Explore solutions written in R based on R Hadoop projects Apply data management skills in handling large data sets Acquire knowledge about neural network concepts and their applications in data mining Create predictive models for classification, prediction, and recommendation Use various libraries on R CRAN for data mining Discover more about data potential, the pitfalls, and inferencial gotchas Gain an insight into the concepts of supervised and unsupervised learning Delve into exploratory data analysis Understand the minute details of sentiment analysis In Detail Data mining is the first step to understanding data and making sense of heaps of data. Properly mined data forms the basis of all data analysis and computing performed on it. This learning path will take you from the very basics of data mining to advanced data mining techniques, and will end up with a specialized branch of data mining—social media mining. You will learn how to manipulate data with R using code snippets and how to mine frequent patterns, association, and correlation while working with R programs. You will discover how to write code for various predication models, stream data, and time-series data. You will also be introduced to solutions written in R based on R Hadoop projects. Now that you are comfortable with data mining with R, you will move on to implementing your knowledge with the help of end-to-end data mining projects. You will learn how to apply different mining concepts to various statistical and data applications in a wide range of fields. At this stage, you will be able to complete complex data mining cases and handle any issues you might encounter during projects. After this, you will gain hands-on experience of generating insights from social media data. You will get detailed instructions on how to obtain, process, and analyze a variety of socially-generated data while providing a theoretical background to accurately interpret your findings. You will be shown R code and examples of data that can be used as a springboard as you get the chance to undertake your own analyses of business, social, or political data. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: Learning Data Mining with R by Bater Makhabel R Data Mining Blueprints by Pradeepta Mishra Social Media Mining with R by Nathan Danneman and Richard Heimann Style and approach A complete package with which will take you from the basics of data mining to advanced data mining techniques, and will end up with a specialized branch of data mining—social media mining.