The “5-Minute Guide to Text Analytics” prepared by Attivio to learn how cognitive solutions surface the untapped business value typically hidden in unstructured content. By automatically identifying key concepts, extracting entities, and analyzing sentiment – with multi-language support – text analytics adds structure to the unstructured so it can be added to a knowledge graph, along with the structured data.
Search Results for: text mining
5-Minute Guide to Text Analytics
August 17, 2017 by Leave a Comment
Surfacing the Untapped Value in Unstructured Content. Download the “5-Minute Guide to Text Analytics” prepared by Attivio to learn how cognitive solutions surface the untapped business value typically hidden in unstructured content.
Big Data Breakthrough: Process Mining
June 29, 2017 by 1 Comment
In this special guest feature, Alexander Rinke, co-CEO and co-founder at Celonis, explains how big data – and more specifically process mining – can help organizations gain full transparency into their operations, in turn allowing them to improve margins, business agility and customer service while reducing operational costs.
Key Challenges for Commercial Text Miners
February 16, 2017 by Leave a Comment
Researchers use text mining tools to extract and interpret facts, assertions, and relationships from vast amounts of published information. Mining accelerates the research process. However, despite the many benefits of text mining, researchers face a number of obstacles before they even get a chance to run queries against the bigger body of literature. Read on as Michael Iarrobino, Product Manager at Copyright Clearance Center, explains the key challenges for commercial text miners.
The Business Value of Deep Text Analytics at Massive Document Scale
February 2, 2017 by Leave a Comment
In this special guest feature, Dr. Brian Sager, CEO and co-founder of Omnity.io, provides 5 examples in support of the business value of deep text analytics at massive document scale. The examples are drawn from use cases within R&D, competitive strategy, patent law, and knowledge management, as well as M&A and post merger integration.
Provalis Research Introduces QDA Miner 5 to Improve Advanced Qualitative Data Analysis of Unstructured Text
December 6, 2016 by Leave a Comment
Provalis Research, a leading provider of text analytics software, announces the release of QDA Miner 5 (Qualitative Data Analysis). This latest version provides enhanced data portability, sharpened analysis of unstructured text and increased visualization capabilities. The new version introduces more than 25 new features that facilitate data import from external sources and uses powerful analysis tools to present results.
Key Challenges for Commercial Text Miners
February 2, 2016 by Leave a Comment
In biomedical research and development, researchers use text mining tools to extract and interpret facts, assertions, and relationships from vast amounts of published information. Mining accelerates the research process, increases discovery of novel findings, and helps companies identify potential safety issues in the drug development process. However, despite the many benefits of text mining, researchers face a number of obstacles before they even get a chance to run queries against the body of biomedical literature.
Text Analytics: The Next Generation of Big Data
June 5, 2015 by Leave a Comment
In this special guest feature, Jeff Catlin of Lexalytics lays out the case for text analytics and its importance to the rising interest in big data. Jeff is CEO of Lexalytics, a company providing sentiment and intent analysis to an array of businesses using SaaS and cloud based technology.
Data Science 101: Mining Big Data with Apache Spark
October 27, 2014 by Leave a Comment
Mining Big Data can be an incredibly frustrating experience due to its inherent complexity and a lack of tools.
Rescuing Lost History: Using Big Data to Recover Black Women’s Lived Experiences
September 20, 2016 by Leave a Comment
“A lot of times when people think about big data, they think about it in ahistorical times…outside of this political context,” said Ruby Mendenhall, an associate professor of sociology at UIUC. “It’s really important to think about whose voice is digitized, in journals and newspapers. A lot of that for black women has been lost and you need to make a concerted effort to recover it.” Mendenhall’s study employs Latent Dirichlet allocation (LDA) algorithms and comparative text mining to search 800,000 periodicals in JSTOR (Journal Storage) and HathiTrust from 1746 to 2014 to identify the types of conversations that emerge about Black women’s shared experience over time.
