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. See the primer for an introduction and the list of posts.
In the previous post, we introduced the basics of text-network analysis using the example of co-occurrence networks. Any application of text-network analysis evolves around two questions: 1. What are my nodes? and 2. what are the relations (edges) between nodes? The answer to the first question is to a large extend the same with different text-network creation methods. The main differences concern the way of identifying edges. Therefore, this post will focus on the second question and only highlight relevant differences in the selection of nodes.
Co-occurrence networks construct edges between nodes based on their proximity in the text. This is a quite rough criterion and error prone: Words close to each other will not always have a meaningful relation to each other, so one gets more edges than desired (false positives). On the other hand, some syntactic arrangements like subordinate clauses can have the effect that related words appear relatively far from each other, and the methods misses relevant edges (false negatives). A better approach thus might take the syntactic structure of the text into account.