Text Analytics: Insights to Enhance Predictive Model by Nina Lahvinovich

Please join us at Hardy Coffee in Benson for a workshop on Using Text Analytics Insights to Enhance Predictive Model by Nina Lahvinovich. Please RSVP so we can plan accordingly.

Schedule:
Networking 5:00-5:30pm
Workshop 5:30-6:30pm

It all started one day when a customer approached me with the question: “What is in this text data? What does it tell me?” As a new analytics team, we had never done Text Mining before, so it was the time to figure it out. It was different this time, since the data didn’t have a predefined structure, hence new methods and approaches were required. Many more customers with same type of questions came since that day, and for all of them we were able to transform plain text data into a structured output, which was used to make decisions and take actions, while taking away a burden of manually reading and comprehending thousands of texts.

It is estimated that about 80% of data owned by the companies today is unstructured, i.e., is stored in the form of plain texts, videos and images. You can find it in emails, word and pdf documents, presentations, phone transcripts, survey results, research papers, and many other sources. This data holds a lot of important information and deep insights about business processes that is mostly untapped today due to its unstructured nature. Text Mining helps extract this information and convert it into the actionable output that can be used by organizations to improve processes and find new efficiencies. This presentation will introduce the basics of Text Mining and demonstrate practical text mining examples using R and SAS Text Miner.

Bio
Nina Lahvinovich is a Systems Engineer/Data Scientist at Union Pacific Railroad. She earned a MS in Management Information Systems with concentration in Data Analytics in 2015, and has been working on data science projects since 2013. Besides other work, she researched and introduced the practice of Text Mining to the company in 2014, which was implemented in R. As company expanded its analytical toolkit, the latest text mining projects were completed using SAS Text Miner.