Semantic Network Analysis in Consumer Research

The analysis of language is fundamental to consumer research. Technological advancements caused the computation al turn in academia, increasing the use of computation al methodological approaches. Semantic network analysis (SemNA) is one such computational method, enabling researchers to visualize, analyze and interpret relationships between words and concepts and identify patterns and trends in unstructured large-scale, text-based data. In more detail, SemNA enables the analysis of text to explore 'meaning networks' or 'webs of meaning'. Processing automated text analysis (ATA) output, SemNA subsequently measures "the extent to which words are related, which indicates something about their meaning" by "the extent to which word pairs co-occur within a given meaning unit" (Rice 2005, 288). SemNA utilizes these measures as the basis for thematic clustering, i.e., modularity calculation (Brandes et al. 2008), which then allows the researcher to engage in a close qualitative reading of the semantic network.