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From Text Mining to Abstract Mining

Drew University’s Data Analytics professor Ellie Small explains it all

March 2023 – What are text mining and abstract mining—and how are they useful tools in medical research?

These questions and many others were answered during a hybrid event featuring Drew University’s Dr. Ellie Small, Norma Gilbert Junior Assistant Professor of Mathematics and Computer Science.

Hosted by Drew’s Caspersen School of Graduate Studies Data Analytics program, attendees had the privilege of learning about text mining, abstract mining, and new methods developed to identify novel ideas for medical research.

Data analytics has seen a surge in growth opportunities, largely due to the availability of data, the need to analyze the data in various ways, and the increased ability to store and analyze data.

“The cost of computer storage has decreased, while computation power has increased,” said Small, who specializes in data science and has completed research papers in networks and text mining. 

Small explained the difference between various types of data analysis: text mining is analyzing text in documents—from one document to thousands of documents—and abstract mining is the ability to analyze multiple words or short phrases in documents.

Utilizing PubMed, a biomedical literature database, Small developed logic to extract frequently occurring phrases from the housed papers and cluster them according to the frequency of the phrases within the papers.

This application of data greatly simplifies medical research for students and the medical community at large.

Alex Rudniy, assistant professor of data analytics, also offered an overview of many usages of the data analytics tools and the industries that utilize these tools—from marketing, travel, health care, and beyond.

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