Providing Insights for Open-Response Surveys via End-to-End Context-Aware Clustering

Abstract

Teachers often conduct surveys in their classes to gain insights into topics of interest. When analyzing surveys with open-ended responses, a teacher traditionally has to read the responses one by one, which is a labor-intensive and time-consuming process. We present a novel end-to-end context-aware framework that extracts, aggregates, and abbreviates embedded semantic patterns in open-response survey data. Our framework uses a pre-trained natural language model to encode the textual data into semantic vectors. The encoded vectors then get clustered either into an optimally tuned number of groups or into a set of groups with pre-specified titles. We provide context-aware wordclouds that demonstrate the semantically prominent keywords within each group. Honoring user privacy, we have successfully built the on-device implementation of our framework suitable for real-time analysis on mobile devices and have tested it on a synthetic dataset. Our framework reduces the costs at-scale by automating the process of extracting the most insightful information pieces from survey data.