Semantic Network Analysis (SemNA): A Tutorial on Preprocessing, Estimating, and Analyzing Semantic Networks

To date, the application of semantic network methodologies to study cognitive processes in psychological phenomena has been limited in scope. One barrier to broader application is the lack of resources for researchers unfamiliar with the approach. Another barrier, for both the unfamiliar and knowledgeable researcher, is the tedious and laborious preprocessing of semantic data. We aim to minimize these barriers by offering a comprehensive semantic network analysis pipeline (preprocessing, estimating, and analyzing networks), and an associated R tutorial that uses a suite of R packages to accommodate the pipeline. Two of these packages, SemNetDictionaries and SemNetCleaner, promote an efficient, reproducible, and transparent approach to preprocessing linguistic data. The third package, SemNeT, provides methods and measures for estimating and statistically comparing semantic networks via a point-and-click graphical user interface. Using real-world data, we present a start-to-finish pipeline from raw data to semantic network analysis results. This article aims to provide resources for researchers, both the unfamiliar and knowledgeable, that reduce some of the barriers for conducting semantic network analysis. Translational Abstract We introduce a pipeline and associated tutorial for constructing semantic networks from verbal fluency data using open-source software packages in R. Verbal fluency is one of the most common neuropsychological measures for capturing memory retrieval processes. Our pipeline allows researchers who are unfamiliar or experienced with semantic network analysis to preprocess (e.g., spell-check, identify inappropriate responses), estimate, and analyze verbal fluency data as semantic networks, revealing the structural features of memory retrieval. The R packages support transparent and reproducible preprocessing of verbal fluency data, providing a standardized approach. Our empirical example demonstrates how these packages can be applied to real-world data. Finally, our pipeline is modular meaning different components (i.e., preprocessing, estimating, and analyzing) can be used in isolation, supporting other semantic tasks (e.g., free association, semantic similarity) as well as cross-software compatibility (e.g., Cytoscape, SNAFU in Python, other R packages).