![]() 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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