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.

#infranodus

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#twinword

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@twinword provides #topic_modeling #sentiment_analysis #word_associations info #lemmatization apis https://www.twinword.com/

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https://www.twinword.com/

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@infranodus provides #topic_modeling and #word_associations info (in process) http://infranodus.com

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@twinword provides #document_similarity analysis

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@datumbox provides #document_similarity #sentiment_analysis #topic_modeling api http://www.datumbox.com/

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in #twinword api #lemmatization works only for english

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@ibm_watson offers #topic_modeling #sentiment_analysis and #entity_extraction https://www.ibm.com/watson/

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@textalizer performs some very basic #topic_modeling

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@dandelion performs #sentiment_analysis #text_classification #entity_extraction #document_similarity via API (SaS) http://dandelion.eu/

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@wordsapi provides #word_associations and #word_definitions via an API, also has an easy-to-use online tryout http://wordsapi.com

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@eventregistry provides #topic_modeling and #concept_graphs for various news and events around the world (also API) http://eventregistry.org/

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#infranodus also provides #concept_graphs

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@rosette #entity_extraction #topic_modeling #name_matching (main project) via API http://rosette.com

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https://www.rosette.com/

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#rosette also provides #lemmatization

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https://www.rosette.com/

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#rosette #lemmatization https://www.rosette.com/

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@monkey_learn provides #entity_extraction and #text_classification API https://monkeylearn.com/

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@meaning_cloud #topic_modeling #sentiment_analysis #text_classification #summarization https://www.meaningcloud.com/

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@text_razor #topic_modeling https://www.textrazor.com/

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@aylien #topic_modeling #entity_extraction #sentiment_analysis #summarization https://developer.aylien.com/

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@paralleldots #sentiment_analysis #entity_extraction #intent_analysis (also via API) https://www.paralleldots.com/

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@summarize_bot #entity_extraction #summarization #lemmatization #bias_analysis https://www.summarizebot.com/

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@lexalytics performs #sentiment_analysis and #text_classification as well as #topic_modeling https://www.lexalytics.com

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@infongen performs #sentiment_analysis and provides #actionable_insights https://www.infongen.com/

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@rapidminer #sentiment_analysis #topic_modeling

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@textgain provides #sentiment_analysis and #topic_modeling and #text_classification

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@monkeylearn provides solutions for #sentiment_analysis and #text_classification as well as #topic_modeling

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#cloudmersive can do #entity_extraction https://cloudmersive.com

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#cloudmersive can also do #sentiment_analysis

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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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    Topics Nodes in Top Topic Components Nodes in Top Comp
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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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