This article proposes an innovative method for the visual analysis of narrative data that involves three steps: transforming narrative data into relational data, creating two-mode networks displayed with graph optimization algorithms derived from social network analysis (SNA), and visually analyzing sociograms. We argue that understanding how actors and their opinions constitute a network-like structure opens up promising avenues for interpreting data. This approach provides powerful data visualization that facilitates inductive identification of the underlying structure of narrative data. It also reveals the complexities of the links between differently positioned actors in a structure that a personal attribute-based analytical method might overlook. Lastly, it can be productively combined with other quantitative and qualitative methods to make sense of narrative data.
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