How Can a Historical News API Help Organizations?
Sometimes getting data in near real-time daily or hourly is not enough. Brands and organizations sometimes need to collect and extract data over a period of time from all over the web to discover trends in the market or make predictions about the future. Before they can do this, however, they first need to receive historical web data from a third-party data provider.
An advanced historical news API includes web data from news, blogs, forums, and online discussions from a massive repository of historical data. It also has advanced search filters that allow businesses to search for information according to specific keywords, or by organization name, publication date, author, domain name, the number of social media shares and likes, and more. Datasets can be sorted by a specific time period as well as by category, like for example positive or negative movie reviews.
Many organizations and brands turn to historical web data for a wide range of purposes. Financial institutions rely on it to identify trends in the stock market with alternative web data. Media and web monitoring organizations use it to predict market trends and develop new products or a business plan that allows them to stay ahead of the competition. Market research companies need it to gather news about brands and their competitors to discover historical patterns and gain insights.
Enterprise-level organizations and researchers alike rely on historical data as a foundation for natural learning processing (NLP), machine learning and sentiment analysis, and other advanced algorithmic models. For example, researchers at Southern Methodist University took a large selection of Webhose’s news datasets to develop a model that would identify fake news more accurately and quickly than a human. They used the term “Hurricane Florence” as the common keyword between all articles to ensure that the data was relevant to their model.
Webhose’s historical web data, known as Archived Web Data, includes over 100TB of historical content going as far back as 2008 and is regularly updated. Its advanced search filters allow for granular filtering capabilities so that organizations and researchers alike can build specific models based on a particular subject, event, language, country, and more. Brands and organizations using Webhose’s historical web data receive high-quality, relevant, machine-readable data delivered to them in XML or JSON format. That way they can then focus their time and resources on their data analysis and developing advanced algorithmic models rather than data preparation.