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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Show Nodes with degree > 0:

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

Most Influential Elements
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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:
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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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%


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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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