Company's activities: A large financial company (+10,000 employees) with a turnover of $1,000,000,000 that provides consulting services in the field of long-term investment.
To build graphs of popularity and users' attitudes (sentiment analysis) to companies and public figures based on public and private sources of information, which would increase the accuracy of forecasting and risk assessment by 75%.
To build an AI/ML solution for:
1. Gather, analyze, store the information in real-time received from the following sources:
Public:
Private:
2. Perform an NLP analysis to identify entities and sentiment value to them with reference to time.
3. Record the information received in separate databases for quick search, analytics and building graphs with reports.
4. Provide a user tool for building graphs of the entity's popularity and users' attitude towards it.
5. One of the key features of this solution is that the flow of these processes is flexible, and an analyst can build the desired flow on his/her own using the user interface (UI), even without any experience with programming languages.
This tool helps not only to collect and structure the information from completely different sources but also to get a complete picture of the changes related to the company's or person's popularity and people's attitude towards them over a long period of time. Moreover, this information can be used for the prediction and assessment of the investment risk.
