Tutorial Overview

When it comes to financial opinion mining, bullish and bearish come into people's minds. However, much fine-grained information will be missed if we only focus on the market sentiment analysis of financial documents. Thanks to the recent "CS + X" trend, much interdisciplinary cooperation exists between computer science and other domains. In the "NLP + Finance" community, lots of recent works pay attention to in-depth analysis of different kinds of financial documents rather than market sentiment prediction. For example, Keith and Stent (2019) [1] analyze the pragmatic and semantic features in the earnings conference calls and discuss how these features influence the investor's decision-making process. Zong et al. (2020) [2] point out the difference between the textual factors and cognitive factors by comparing the accurate and inaccurate professional analysts' reports. Our previous works find that (1) the numeral information extracted from financial social media data is helpful for trading [3], (2) evaluating the quality of opinions can help us select profitable reports [4]. The above-mentioned works conclude the necessity of capturing fine-grained opinions in the financial narratives. As the increasing interest of our community on this topic, recently, more and more related workshops spring up in the leading conferences, including FinNLP-2020 in IJCAI, FinIR-2020 in SIGIR, and FNP-2020 in COLING.

In this tutorial, we will show where we are and where we will be to those researchers interested in this topic. We divide this tutorial into 3 parts, including coarse-grained financial opinion mining, fine-grained financial opinion mining, and possible research directions. This tutorial starts by introducing the components in a financial opinion proposed in our research agenda [5] and summarizes their related studies. The audience of this tutorial will gain an overview of financial opinion mining. We hope that this tutorial will help participants to figure out their research directions.

[1] Keith, Katherine, and Amanda Stent. "Modeling Financial Analysts’ Decision Making via the Pragmatics and Semantics of Earnings Calls." Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019.

[2] Zong, Shi, Alan Ritter, and Eduard Hovy. "Measuring Forecasting Skill from Text." Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020.

[3] Chen, Chung-Chi, et al. "Crowdpt: Summarizing crowd opinions as professional analyst." The World Wide Web Conference. 2019.

[4] Chen, Chung-Chi, Hen-Hsen Huang, and Hsin-Hsi Chen. "Evaluating the rationales of amateur investors." Proceedings of the Web Conference 2021. 2021.

[5] Chen, Chung-Chi, Hen-Hsen Huang, and Hsin-Hsi Chen. From Opinion Mining to Financial Argument Mining. Springer Nature, 2021.