Stephanie Kim, Algorithmia
Stephanie Kim tries to build useful things and enjoys finding stories in data using Python and R. Her focus is using tools such as NLP and data mining in an attempt to discern users’ trouble spots in unstructured text data to improve the overall user experience of a product. She is currently spending her time at Algorithmia, a marketplace focused on enabling AI in every application, as their Developer Advocate. She is the founder of Seattle PyLadies and loves learning about things she’s not very good at.
Investigating User Experience with Natural Language Analysis
This talk focuses on the methodology and intent of studying feedback form data using Python tools and libraries for natural language processing and machine learning analysis. I will discuss potential trouble areas of starting such a project from scratch from a developer’s perspective. This will include the type of analysis that might be helpful in discerning user experience and what analysis that you run, but might end up tossing out at the end due to lack of insight on your data. For instance, what is the value of running a K-Means cluster analysis and does it offer substantial actionable insights for textual content? And what happens when your logistic regression falls short of expectations?
My intent is that people will walk away learning the basics of textual analysis and become motivated to help their users succeed in whatever tasks they are trying to accomplish through finding potential points of friction and even issues that spring up from changes in the design. This talk will be for developers or marketers who don’t have a lot or any experience in data analysis or machine learning.