In the media sector, new business models are needed due to digitalization. Press releases are enriched daily with metadata. This service is provided by an external service provider. The metadata are collected using intelligent text analytics. In addition, pattern recognition is applied to the metadata to generate new information, which can be processed using predictive algorithms to forecast the probability of occasions in the future.
Supplement
Millions of newspaper articles and trade journals are tagged with found events/signals. These can include: Change of decision-maker, new location of a company, mergers & acquisitions etc., as well as companies, locations (district, city, region, state etc.), persons (CEOs of companies) and industries (by WZ code). Process: The data supplier sends press and trade journal data (XML data) to the service provider. The data is examined and tagged as described above. Text analytical products are used here, among other tools. The 'enriched' messages can then be conveniently located and accessed in a portal. To enable this, the customer formats the metadata in an active search for the user.
Subject description
Thanks to the intelligent detection mechanisms for companies and occasions from more than 1000 up-to-date news sources, relevant risks and opportunities are no longer missed. To be precise, not even the announcement of a risk or opportunity is missed now. Text analytics are used to search accurately, using predefined opportunity and risk signals, through message sources that are updated on a daily basis. These include, for example, companies in regional newspapers that are announcing a recruitment freeze for trainees, a company that is planning to open or close a production site, or add or discontinue a product line. Even if arguments are made in general in the industry about the positive or negative impact of regulators or tax burdens as a benefit or disadvantage of a location, this information can be correlated with a company.