• Landscape Reading Model — Text Network Representation

    How do we perceive a text when we read it? The answer to this question can help us understand how attention functions and identify various strategies to enhance our writing. Additionally, it can inspire new methods of reading that increase engagement and accessibility. In this article, we’re going to demonstrate how the landscape reading model can be represented using text networks and how this representation helps generate new ideas and insights in InfraNodus.

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  • Mind Map AI Generator

    In this article, we will present an AI mind map generator — InfraNodus — that can create a knowledge graph from any text. Unlike other tools, it doesn’t start from a central idea, because that’s not how our mind works. Rather, it maps out all the ideas present within a text as a network graph, so the resulting mind map will be more rhizomatic, helping you see the patterns rather than presenting hierarchies (which often do not even exist).

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  • Sentiment Analysis: AFINN vs Bert AI Algorithms (using the Twitter and Amazon examples)

    A comparison of text network analysis, dictionary-based AFINN, and machine-learning based BERT AI models for sentiment analysis of customer reviews.

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  • Discover EU Grant Opportunities using Network Analysis

    In this case study, we will demonstrate how you can use text network analysis to analyze EU grant opportunities. We will demonstrate how you can use the graph to identify the most relevant topics in the existing grant proposals. We will also show how you can use network visualization to identify the structural gaps within.

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  • Open Call: Network Analysis Case Studies

    If you are interested in networks, graphs, data science and text analysis we invite you to take part in this open call.

    We invite you to write an article with a case study using InfraNodus network visualization and analysis tool for any task or research practice.

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