Tracy Tuten's List: MarketingResearch

• Stanford computer scientists have created a website that gives anyone who can cut and paste the ability to answer such questions, systematically and for free.

   

  The website is known as etcML, short for Easy Text Classification with Machine Learning.

• Machine learning is a field of computer science that develops systems that give computers the ability to acquire new understandings in a more human-like way.

   

  The etcML website is based on machine-learning techniques that were developed to analyze the meaning embodied in text, then gauge its overall positive or negative sentiment. To access this computational engine, users drag and drop text files into a dialog box.

• “We wanted to make standard machine learning techniques available to people and researchers who may not be able to program,” said Richard Socher, a doctoral candidate in computer science at Stanford and lead developer of etcML.

   

  Socher said the new site gives researchers and citizen activists in fields ranging from political science to linguistics an easy way to analyze news articles, social media posts, closed-caption transcripts of television newscasts and other texts of possible interest.

• Rob Voigt, a researcher in computational linguistics at Stanford, has used etcML to evaluate pitches on Kickstarter, a website that provides a platform for artists, entrepreneurs and others who are seeking financial backing for their projects.

   

  Voigt studies what makes a successful pitch. Using etcML, he has found that pitches using plural pronouns – we, us, our – fare better than those written in the first-person singular. Likewise, short films seem to have done better than projects involving comic books, games or fashion.

• Chinmay Kulkarni, a doctoral student in computer science at Stanford, used etcML to help grade short answer tests for a free, online course with roughly 2,000 students. Testing for the online course presented a challenge: Multiple-choice exams were easiest to grade automatically, but short answers offered a better measure of learning. Yet the instructor couldn’t possibly read and grade 2,000 tests.

   

  To solve this problem, students taking the course were required to grade one another. On average, four students ended up grading each exam. This increased the workload on each student. Kulkarni used etcML to help out. The software graded each test. Students still graded one another. But with the software in the grading loop, the average exam only had to be read by three or fewer students.

3 more annotations...

• In this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.