QuAD: Deep-Learning Assisted Qualitative Data Analysis with Affinity Diagrams
Ariel Goldman, Cindy Espinosa, Shivani H. Patel, Francesca Cavuoti, Jade Chen, Alexandra Cheng, Sabrina Meng, Aditi S. Patil, Lydia B. Chilton, Sarah Morrison-Smith · CHI Conference on Human Factors in Computing Systems Extended Abstracts · 2022
Affinity diagramming is an effective and efficient method for forming nuanced interpretations of wide-ranging, unstructured qualitative data; however, this method does not scale well to large data sets. We propose a novel affinity diagramming system, called Qualitative Affinity Diagrammer (QuAD) that leverages computer-generated suggestions using deep learning to address the scalability of the diagramming process. QuAD features automatic grouping suggestions to jump-start the affinity diagramming process and provides grouping suggestions throughout the diagramming process to reduce sifting through notes. In this paper, we present a prototype of QuAD that uses Bidirectional Encoder Representations from Transformers (BERT) and Girvan-Newman to generate grouping suggestions. This work is the first step towards creating a powerful tool for assisting in the analysis of large qualitative data sets in a variety of contexts, including human-computer interaction.