Analyzing Open-Ended Survey Questions Using Unsupervised Learning Methods

Unsupervised learning methods such as topic modeling or k-means clustering can provide techniques for organizing, understanding and summarizing text data without using any manually labeled records as training data. It uses annotations to organize text and discover latent themes in documents without target attributes. We explore using unsupervised learning to classify open-ended survey question responses. By grouping similar responses together, we construct a class of “topics” which are described by sets of