Information sourcing and research is a common problem for many companies out there.
Detecting Company Management Structure Changes
Let’s focus on a particular, real-life example.
An investor needed information about changes in the management structure of companies in NYSE. Before leveraging the AI solution, he employed a dedicated team of research analysts traversing thousands of web articles, tweets and social media posts looking for recent changes in the structure of the companies.
The first step we need to do is to aggregate data from the valuable sources. So far, our client’s analysts read online business newspapers and monitored selected Twitter accounts looking for structural change information.
Our system has entirely automated that work by scraping newspapers and tweets, producing a steady feed of information ready to use by the analysts, putting the information in one place.’
As the next stage in the pipeline, our state-of-the-art AI text classification algorithms precisely filter out all the irrelevant materials, basing their judgment on the context and actual meaning of the text.
We’ve already managed to save a lot of time by showing only the most valuable information.
However, there’s still a lot of work to do. Working on raw documents is quite difficult. We need to extract the information that the client needs.
And AI can help us in that as well!
Data scientists call this technique named entity extraction. It identifies important information to the investor in the text, based on given criteria. In this case, we extract the company name, position, and the reason, and put it in a table row that we can use later.
At this point, it can be exported into JSON or CSV.
You probably know that the computer is great in processing tabular data, like Excel tables or databases. We call this type of information structured data.
The AI now further processes this data by:
Our investor needs human-like, readable reports that he/she can easily relate to and quickly find important information.
Thanks to another technology called the Natural Language Generation (NLG), the artificial intelligence can generate human-sounding reports and summaries.
And we’re going to leverage this new machine skill to help our investor.
Our last step is to turn the raw table into an executive summary that the client can use to guide their further decisions.
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