• Variability of a Text, Ecology of Attention

    Modulating variability in a text or a conversation can have an interesting effect on its dynamics. A writer will never get stuck, a conversation will always be interesting, a discourse will stay ecological at its core. We propose an approach based on panarchy, embracing diversity and homogeneity at the same time, as applied to discursive practices.

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  • Text as a Musical Score using InfraNodus and Orca

    Using text network visualization we can convert any text into a musical score, which can be used to play music live. This case study demonstrates how this can be done in a VR space.

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  • Word Cloud Generator Based on Text Network Visualization

    Word clouds provide a quick and visually appealing way to showcase textual content and they have been in use since the 90s. However, they lack precision, interactivity, and context. We developed a methodology and a word visualization tool that can take word clouds to the next level, making them much more useful not only for visualization, but also for research and educational purposes.

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  • Tutorial: Text Mining Using LDA and Network Analysis

    In this tutorial we present a method for topic modeling using text network analysis (TNA) and visualization. The approach we propose is based on identifying topical clusters in text based on co-occurrence of words. We will demonstrate how this approach can be used for topic modeling, how it compares to Latent Dirichlet Allocation (LDA), and how they can be used together to provide more relevant results.

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  • Using Efficient Search Methods for Content Structuring

    Designing efficient content structures that guide readers through in ways that are efficient, maintaining diversity and narrative of the original.

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