![]() ![]() Addresses to the Federal Assembly of the Russian Federation by Russian presidents, 2008–2012: comparative analysis, Russian Journal of Communication, Volume 5, Issue 3 Direct Visual Feedback on the Process of Ideation using Text Network Graphs Encourages a more Coherent Expression of Ideas, Nodus Labs InfraNodus: Generating Insight Using Text Network Analysis You can also check our page on Google Scholar. If we then click on “technology”, we get an excerpt of the text that contains those two terms simultaneously: talking about the sustainable energy development.īelow you will find a list of our scientific publications. When we select a specific word, for example “nation”, the graph will show us all the words it’s connected to. So the only thing you can do is to click on “Nation” and see all the other political speeches that contain this term: that is, all of them □Īlternatively, a text network graph can provide a much more precise way to search through the text or through the corpus of text. We often see word clouds that are used in navigational elements: click on the term and you’ll see all the articles tagged with this term. However, they can also be used to provide the interactive features, which the standard word clouds lack. You can use it to make sense of disjointed bits and pieces of information, get visual summaries for text documents, and generate insight for your research process: The Interactive Word Cloud Text NetworkĪs we’ve shown above, text network visualizations generate more precise word clouds that take the context into account. Try InfraNodus Text Network Visualization Tool developed by Nodus Labs. We will simply copy and paste the text into the system to visualize it as a network: To visualize that same text as a network we will use the open-source tool InfraNodus. While there are many different ways to do that, text network visualization offers a powerful way to achieve that because it is based on the graph theory, providing the quantitive base layer to the qualitative insights we can derive from visualizations. the bigrams), so we can see which words tend to appear next to each other. One way to do that is to take into account the words’ co-occurrences or the n-grams (e.g. Then we can see not only the most influential words but also how they are used together, providing a much better overview of the text’s meaning. So, how can we improve the word clouds? The best place to start is to introduce the context. Text Network Visualization: Word Cloud with a Context ![]() This post on Thematic describes all the shortcomings in more detail, but it is clear that word clouds are oversimplified and should probably only be used for decorative purposes. Just a collection of disjointed terms without a context. We can understand from the picture above that Obama had been talking about the “people”, “american”, “citizens”, as well as something about “requires” and “freedom” but that’s about it. The most frequently mentioned words are bigger, ranged in the alphabetic order. ![]()
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