IBM has unveiled new SPSS Modeler data mining and text analytics software that enables users to uncover and analyse information from social media sources, such as social networks and blogs, and then integrate them with internal data for faster insight and predictive intelligence.
IBM said that the new software allows users to monitor changes in consumer, constituent and employee attitudes, bring out deeper insights, and then predict key factors that will drive customer acquisition and retention campaigns.
The new software analyses trends and captures insights from industry-specific terminology and includes new semantic networks with 180 vertical taxonomies, and more than 400,000 terms, including 100,000 synonyms and thousands of brands, allowing customers to draw better links and understanding, the company said.
According to IBM, the new predictive analytics software allows customers to directly access text, web and survey data and integrate it into predictive models for recommendations and better business decisions. It uses natural language processing (NLP) to allow clients pull key concepts, opinions and categories relevant to their business.
In addition, organisations can also combine all of their structured data with textual information from documents, e-mails, call centre notes, and social media sources. Users can extract, discover and explore relationships between concepts and sentiments, including emoticons and slang terminology, by incorporating text sources into modeling efforts.
IBM predictive analytics can be combined with the entire portfolio of IBM technologies to improve their current business and operational environments.
The new version of IBM SPSS Modeler data mining and text analytics workbench is now available worldwide, while the Text analytics workbench is available in IBM SPSS Modeler Premium edition.
