Text Network Analysis

  • 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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  • Text Network Visualization of Video Subtitles

    How to visualize the subtitles of any video and to get to the most essential parts of a talk or a lecture, find the parts that you’re interested in, and to generate new and relevant ideas based on that discourse.

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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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  • News Analysis Using Text Network Visualization of RSS Feeds

    Text network analysis can be used to better understand the news picture of the day and to analyze the current media discourse.

    In this case study we import RSS news feeds of the major new outlets to visualize their discourse and to compare their approach.

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  • Conference Talks Content Profiling Using Text Network Analysis

    In this case study we demonstrate how text network analysis can be used to identify the main topical threads of a conference that consists of several talks. We also show how it can be used to identify how closely those talks are related as well as to suggest collaborations between the speakers or relevant thematic threads for the audience.

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