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Evaluating Representativeness of Qualitative Text Data in Identifying UX Issues

ABSTRACT With increasing recognition of user experience (UX), it became important to identify UX issues from customers’ opinions expressed in a form of text that is unstructured qualitative data. Since reliability of qualitative data has little been investigated, it requires much time and effort for researchers to define meanings and values from the qualitative data. Therefore, this article proposes a novel approach to measuring the stability of textual data by adopting semantic network analysis, as shown by our analysis on in-depth interview transcripts and online customer review data. Among the semantic networks generated from text, subnetworks were sampled from the original networks until the representativeness of each sample size was determined. Then, similarities between the subnetworks and the original network were calculated by applying a correlation analysis to determine whether stability occurs. The results indicated that variables of word frequency and network-level statistics affect stability of the network. The suggested method will be able to assist in identifying UX issues.