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.
Tip: use the form below to save the most relevant keywords for this search query. Or start writing your content and see how it relates to the existing search queries and results.
Tip: here are the keyword queries that people search for but don't actually find in the search results.

#wordclouds generates #tag_clouds http://wordclouds.com

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#wordart generates #tag_clouds but its applications are more suitable for entertainment purposes https://wordart.com/

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#tagcroud is an old #tag_clouds generator that has some interesting settings https://tagcrowd.com/

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@wordcloud is an @open_source @python project that creates #tag_clouds https://amueller.github.io/word_cloud/

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https://amueller.github.io/word_cloud/

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@infranodus is an #open_source @text_network_analysis tool that visualizes any text as a network and shows the relations of terms to each other

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#infranodus generates text graphs which can be similar to #tag_clouds http://infranodus.com

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#vader is an #open_source tool that can perform #sentiment_analysis https://www.nltk.org/_modules/nltk/sentiment/vader.html

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https://www.nltk.org/_modules/nltk/sentiment/vader.html

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@rapidminer is a computer application (no cloud version), which helps perform statistical analysis of data and it also has a #text_network_analysis module https://rapidminer.com/

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#Voyant_Tools is a great tool for text analysis. It works online and offers #tag_clouds as well as interesting statistics and #top_keywords detection https://voyant-tools.org

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@lexos is an educational tool that can detect #top_keywords perform #topic_modelling using kmeans clustering and provides a simple #tag_clouds functionality http://lexos.wheatoncollege.edu/

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@overviewdocs is a text analysis and visualization tool that generates #tag_clouds and provides terms extraction based on #tf_idf algorithm https://www.overviewdocs.com

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@aylien api demo can be used for #topic_modelling and has some #tf_idf functionality as well as #sentiment_analysis https://developer.aylien.com/text-api-demo

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@text_network_analysis can be used for #topic_modelling and has similar functionality to #tf_idf

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#infranodus has #tf_idf statistics

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#voyant_tools has similar #tf_idf functionality

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#top_keywords #tf_idf

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@wtfcsv visualizes #tag_clouds and performs basic #topic_modelling for #csv and #excel files

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@infranodus works with #csv and #excel files

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    Main Topical Groups:

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    The topics are the nodes (words) that tend to co-occur together in the same context (next to each other).

    We use a combination of clustering and graph community detection algorithm (Blondel et al based on Louvain) to identify the groups of nodes are more densely connected together than with the rest of the network. They are aligned closer to each other on the graph and are given a distinct color.
    Most Influential Elements:
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    +     Reveal Non-obvious   ?

    We use the Jenks elbow cutoff algorithm to select the top prominent nodes that have significantly higher influence than the rest.

    Click the Reveal Non-obvious button to remove the most influential words (or the ones you select) from the graph, to see what terms are hiding behind them.

    The most influential nodes are either the ones with the highest betweenness centrality — appearing most often on the shortest path between any two randomly chosen nodes (i.e. linking the different distinct communities) — or the ones with the highest degree.

    Network Structure:
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    Action Advice:
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    Structural Gap
    (ask a research question that would link these two topics):
    N/A
    Reveal the Gap   ?
     
    A structural gap shows the two distinct communities (clusters of words) in this graph that are important, but not yet connected. That's where the new potential and innovative ideas may reside.

    This measure is based on a combination of the graph's connectivity and community structure, selecting the groups of nodes that would either make the graph more connected if it's too dispersed or that would help maintain diversity if it's too connected.

    Latent Topical Brokers
    :
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    These are the latent brokers between the topics: the nodes that have an unusually high rate of influence (betweenness centrality) to their freqency — meaning they may appear not as often as the most influential nodes but they are important narrative shifting points.

    These are usually brokers between different clusters / communities of nodes, playing not easily noticed and yet important role in this network, like the "grey cardinals" of sorts.

    Emerging Topics
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    Top Relations
    :

    ⤓ Download   ⤓ Directed Bigrams   ?

    The most prominent relations between the nodes that exist in this graph are shown above. We treat the graph as undirected by default as it allows us to better detect general patterns.

    As an option, you can also downloaded directed bigrams above, in case the direction of the relations is important (for any application other than language).

     
    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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    Please, enter a search query to visualize the difference between what people search for (related queries) and what they actually find (search results):

     
    We will build two graphs:
    1) Google search results for your query;
    2) Related searches for your query (Google's SERP);
    Click the Missing Content tab to see the graph that shows the difference between what people search for and what they actually find, indicating the content you could create to fulfil this gap.
    Find a market niche for a certain product, category, idea or service: what people are looking for but cannot yet find*

     
    We will build two graphs:
    1) the content that already exists when you make this search query (informational supply);
    2) what else people are searching for when they make this query (informational demand);
    You can then click the Niche tab to see the difference between the supply and the demand — what people need but do not yet find — the opportunity gap to fulfil.
    Please, enter your query to visualize the search results as a graph, so you can learn more about this topic:

     
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