Top keywords (global influence):
Top topics (local contexts):
Explore the main topics and terms outlined above or see them in the excerpts from this text below.
See the relevant data in context: click here to show the excerpts from this text that contain these topics below.

this is a word

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this is a phrase with the few words in it

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when there is a few more words in a phrase and I talk about something else than this graph is going to grow

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if I say now something completely different that uses a different vocabulary is going to appear somewhere else in this window

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no if I say a phrase that has both the word vocabulary and window and the graph than these two parts will be connected

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if I say something now about vocabulary for example and use the word Ireland then I can say that the distort connected and this island is the island of topics that are used in the text

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if I now talk about an island that has a lot of trees and it has a sea and beaches then you will see that this statement will make a new picture on the graph because it uses different terms

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in graph:
     

    Show Nodes with degree > 0:

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    Show Contexts:


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    network ecology:
           
    Main Topical Groups
    (graph communities):
    N/A

    Most Influential Elements
    N/A

    Insight Question
    (structural gap):
    N/A

    Stats:
    total
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    nodes, graph density
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    , average degree:
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    Network Structure:
    N/A
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    modularity,
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    % of words in the top topic (total
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    topics),
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    % in the main connected component (
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    in total), influence dispersal
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    %


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    Main Topics
    (according to Latent Dirichlet Allocation):
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    Most Influential Words
    (main topics and words according to LDA):
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    LDA works only for English-language texts at the moment. More support is coming soon, subscribe @noduslabs to be informed.

     
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