Coarse-grained financial opinions can be separated into two (bullish/bearish or positive/negative) or three (bullish/bearish/neutral or positive/negative/neutral) classes, and each opinion related to one target entity.

Coarse-Grained Financial Opinion

Fine-grained financial opinions can be seen as an extension of coarse-grained financial opinion, potentially in the following aspects:
- Analysis aspect
- Degree of sentiment
- Investor's claims
- Market information
- Validity time period

Fine-grained Financial Opinion

Financial opinion can be separated into two main aspects:
- Investor's opinion: the opinion about financial instruments
- Customer's opinion: the opinion about the financial product or financial service

Based on the two opinion types, we can also define the opinions by both coarse-grained and fine-grained viewpoints.

University of Michigan - Ann Arbor

Natural language processing serves the purpose of allowing financial analysts to obtain relevant information through information filtering. Also, in the financial services industry, client communication is imperative this sector, and NLP tools provide banks with critical information when they interact with customers.

Natural Language Processing in  Finance

Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen. 2020. Fine-grained Financial Opinion Mining: A Survey and Research Agenda. arXiv:2005.01897 [cs.CL]. https://arxiv.org/pdf/2005.01897.pdf.

Fine-grained Financial Opinion Mining: A Survey and Research Agenda

Utilizing textual data to improve modeling of the financial market dynamics has long been the tradition of trading practice. The growing volume of financial reports, press releases, and news articles also galvanizes the wish to run this analysis automatically to keep a competitive business advantage. As a result, articles that leverage NLP techniques to predict financial markets are fast accumulating, gradually establishing the research field of natural language based financial forecasting (NLFF), or from the application perspective, stock market prediction.

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