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.
Chung-Chi Chen is a Ph.D. candidate in the Department of Computer Science and Information Engineering at National Taiwan University and a lecturer in the Department of Quantitative Finance, National Tsing Hua University. His research focuses on financial opinion mining and numeral understanding. He is the organizer of FinNum shared task series in NTCIR (2018-2020) and the FinNLP workshop series in IJCAI (2019-2020). He won the 1st prize in both the Jih Sun & Microsoft FinTech Hackathon (2019) and the Standard Chartered FinTech competition (2018) and the 2nd prize in both the Jih Sun & Microsoft FinTech Hackathon (2018) and the E.SUN FHC FinTech Hackathon (2017).
Hen-Hsen Huang received the Ph.D. degree in Computer Science and Information Engineering from National Taiwan University, Taiwan. Dr. Huang is currently an assistant professor in the Department of Computer Science at National Chengchi University. His research interests include natural language processing, computational linguistics, and information retrieval. His work has been published in SCI/SSCI journals and international conferences, including WWW, IJCAI, ACL, CIKM, and COLING. Dr. Huang's award and honors include the Honorable Mention of Doctoral Dissertation Award of ACLCLP in 2014 and the Honorable Mention of Master Thesis Award of ACLCLP in 2008. He serves as the registration chair of TAAI 2017, and as PC members of ACL2018, NAACL 2018, ACL 2017, COLING 2016, NAACL 2016, and ACL 2015, and will be general co-chair of SIGIR 2023.
Hsin-Hsi Chen received the Ph.D. degree in electrical engineering in 1988 from National Taiwan University, Taiwan. He is a distinguished professor in Department of Computer Science and Information Engineering, National Taiwan University. His research interests are natural language processing, information retrieval and extraction, and web mining. Dr. Chen served as senior PC members of ACM SIGIR 2006, 2007, 2008 and 2009, area/track chairs of AAAI2020, AACL 2020, EMNLP 2018, ACL 2012, ACL-IJCNLP 2009 and ACM CIKM 2008, and PC members of many conferences. He received Google research awards in 2007 and 2012, awards of Microsoft Research Asia in 2008 and 2009, MOST Outstanding Research Award in 2017, and the AmTRAN Chair Professorship in 2018.
