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This post is part of a series of posts about text network analysis. Each post will discuss one approach to algorithmically creating networks from texts.Historical network analysis—or, more generally, network analysis in the digital humanities—is in most cases an application of Social Network Analysis in the fields of history or literary studies. It shares with sociological approaches its focus on social actors, or: people. In contrast to sociology, DH applications mostly focus on dead (history) or fictional (literary studies) people.If you are interested in these approaches, there are great examples of historical and literary applications of social network analysis. And don’t forget to check out the Historical Network Research platform.In the SeNeReKo project, we chose a different starting point. Based in the study of religions, we did not want to only focus on (historical of fictional) social structures, but wanted to study the religious content associated with them. So we needed to include the semantics of our sources in the networks we study. ((Here, “semantics” is used not in a linguistic sense, but rather in the sociological sense as opposed to “social structure.”)) The idea is to represent meaning as a network of related concepts. In most cases, this means building networks of words rather than of people. A text, or a collection of texts, is represented as a network, with the network structure (hopefully) revealing its meaning structure.In the project, we experimented with different approaches to text network analysis. In this blog post series, we will describe them and discuss some of the methodological aspects as well as implementation questions.Text networks, part one: PrimerText networks, part two: Cooccurrence networksText networks, part three: Dependency networksText networks, part four: Word context networksText networks, part five: Distribution networksText networks, part six: Topicmodelled networks​