[] [heart]
The physiological functions of the @heart are mainly to govern the #blood and control the #mind, and sometimes to control #emotions and #feelings
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InfraNodus
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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.
[] [lung]
The function of the @lung mainly depends on the propelling and #fixate functions of lung - #qi and on the #moisten function of lung- #yin.
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[] [lung]
The @lung may dominate the #qi and manage the regulation of #water passage and govern the #skin.
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[] [heart]
The @heart governs the #vessels, opens into the #tongue, with external manifestation on the #face.
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[] [pericardium]
@Pericardium #heart
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[] [skin]
The main task of the @skin is to protect the #body, #excrete sweat, and #adjust the body #temperature.
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[] [lung]
The @lung governs the #hair, i.e. the lung has its external manifestation on the hair. The lung opens into the #nose for #smelling.
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[] [liver]
The @liver stores #yin - #blood and has a function as #yang because #yang - #qi in the liver is very active and resolute, tending to disperse.
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[] [liver]
The @liver possesses 2 physiological functions: the first is to #govern dredging and #regulate materials (including #blood, #metabolism, #yin, #yang, and #qi, etc.) of the body (it is called shu-xie function in TCM), and the second is to #store #blood.
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[] [liver]
@liver can also assist the #spleen and the #stomach to digest food by modifying the spleen- #qi and the stomach- #qi with its #dredge and #regulate functions
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[] [liver]
the @liver can #control #mental activity such as #joy, #anger, #anxiety, and #contemplation
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[] [liver]
the @liver can regulate #menstruation in women.
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[] [liver]
The @liver governs the #tendons and opens into the #eyes. The external manifestation of the liver is on the #nails.
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[] [spleen]
The @spleen is loc#ated in the #abdomen #and governs #digestion #and #absorption. As the source of #qi, #blood, #and body #fluid, the spleen plays a vital role in maintaining life activities.
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[] [spleen]
The @spleen governs #transportation and #transformation, it means the spleen can #digest food, #absorb nutrients of food and water, and then #transport them to the #heart and the #lung.
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[] [spleen]
The @spleen can command #blood as to control the blood #circulation inside the #vessels and prevent it from flowing out of the vessels and govern the #muscles and the 4 #limbs. The spleen opens into the #mouth, and its external manifestation is on the #lips.
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[] [kidney]
The @kidney #stores essence and the essence transforms #qi and produces #blood. It is the source of genuine #yin and genuine #yang, and it is related with the #essence, #qi, #blood, #yin, and #yang.
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[] [kidney]
The physiological functions of the @kidney are composed of 6 parts: to #govern #growth and #development, to govern #reproduction, to govern #water (modify and maintain all fluid of the body), to govern #reception of #qi, to produce #marrow to enrich the #brain and transform #blood, and to #nourish and #warm the internal #organs.
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[] [bones] [] [kidney]
The @bones (including the growth of teeth) are governed by the @kidney. It opens into the #ears, the external #genitals, and the #anus, and their external manifestation is on the #hair.
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#small_intestine #heart
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#large_intestine #lung
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#stomach #spleen
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#bladder #kidney
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#gallbladder #liver
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#liver (wood) promotes the function of #heart (fire)
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#heart promotes the function of the #spleen (earth)
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#spleen promotes the function of the #lung (metal)
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#lung promotes the function of the #kidney (water)
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#kidney promotes the function of the #liver
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#gallbladder is paired with #liver and absorbs its excess
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#bladder regulates the output of #kidney
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#small_intestine - absorbs nutrients which are then sent to and pumped through the body by the #heart
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#stomach #spleen
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#large_intestine #lung
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#liver (wood) restrains the #spleen (earth)
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#spleen (earth) restraining #kidney (water)
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#kidney (water) restraining #heart (fire)
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#heart (fire) restraining #lung (metal)
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#lung (metal) restraining #liver (wood)
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the source of this material is the study by Liao W. · Dang C. · Pan W. (2017) — The Brief Theory of Viscus and Its Manifestations in Traditional Chinese Medicine https://www.karger.com/Article/FullText/455853
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the source of this material is the study by Dr. Weidong Pan — The Brief Theory of Viscus and Its Manifestations in Traditional Chinese Medicine https://www.karger.com/Article/FullText/455853
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The higher is the network's structure diversity and the higher is the alpha in the influence propagation score, the higher is its mind-viral immunity — that is, such network will be more resilient and adaptive than a less diverse one.
In case of a discourse network, high mind-viral immunity means that the text proposes multiple points of view and propagates its influence using both highly influential concepts and smaller, secondary topics.
In case of a discourse network, high mind-viral immunity means that the text proposes multiple points of view and propagates its influence using both highly influential concepts and smaller, secondary topics.
The higher is the diversity, the more distinct communities (topics) there are in this network, the more likely it will be pluralist.
The network structure indicates the level of its diversity. It is based on the modularity measure (>0.4 for medium, >0.65 for high modularity, measured with Louvain (Blondel et al 2008) community detection algorithm) in combination with the measure of influence distribution (the entropy of the top nodes' distribution among the top clusters), as well as the the percentage of nodes in the top community.
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Narrative Influence Propagation:
? The chart above shows how influence propagates through the network. X-axis: lemma to lemma step (narrative chronology). Y-axis: change of influence.
The more even and rhythmical this propagation is, the stronger is the central idea or agenda (see alpha exponent below ~ 0.5 or less).
The more variability can be seen in the propagation profile, the less is the reliance on the main concepts (agenda), the stronger is the role of secondary topical clusters in the narrative.
The more even and rhythmical this propagation is, the stronger is the central idea or agenda (see alpha exponent below ~ 0.5 or less).
The more variability can be seen in the propagation profile, the less is the reliance on the main concepts (agenda), the stronger is the role of secondary topical clusters in the narrative.
We plot the narrative as a time series of influence (using the words' betweenness score). We then apply detrended fluctuation analysis to identify fractality of this time series, plotting the log2 scales (x) to the log2 of accumulated fluctuations (y). If the resulting loglog relation can be approximated on a linear polyfit, there may be a power-law relation in how the influence propagates in this narrative over time (e.g. most of the time non-influential words, occasionally words with a high influence).
Using the alpha exponent of the fit (which is closely related to Hurst exponent)), we can better understand the nature of this relation: uniform (pulsating | alpha <= 0.65), variable (stationary, has long-term correlations | 0.65 < alpha <= 0.85), fractal (adaptive | 0.85 < alpha < 1.15), and complex (non-stationary | alpha >= 1.15).
For maximal diversity, adaptivity, and plurality, the narrative should be close to "fractal" (near-critical state). For fiction, essays, and some forms of poetry — "uniform". Informative texts will often have "variable + stationary" score. The "complex" state is an indicator that the text is always shifting its state.
Using the alpha exponent of the fit (which is closely related to Hurst exponent)), we can better understand the nature of this relation: uniform (pulsating | alpha <= 0.65), variable (stationary, has long-term correlations | 0.65 < alpha <= 0.85), fractal (adaptive | 0.85 < alpha < 1.15), and complex (non-stationary | alpha >= 1.15).
For maximal diversity, adaptivity, and plurality, the narrative should be close to "fractal" (near-critical state). For fiction, essays, and some forms of poetry — "uniform". Informative texts will often have "variable + stationary" score. The "complex" state is an indicator that the text is always shifting its state.
Degree Distribution:
? (based on kolmogorov-smirnov test) ? switch to linear
Using this information, you can identify whether the network has scale-free / small-world (long-tail power law distribution) or random (normal, bell-shaped distribution) network properties.
This may be important for understanding the level of resilience and the dynamics of propagation in this network. E.g. scale-free networks with long degree tails are more resilient against random attacks and will propagate information across the whole structure better.
This may be important for understanding the level of resilience and the dynamics of propagation in this network. E.g. scale-free networks with long degree tails are more resilient against random attacks and will propagate information across the whole structure better.
If a power-law is identified, the nodes have preferential attachment (e.g. 20% of nodes tend to get 80% of connections), and the network may be scale-free, which may indicate that it's more resilient and adaptive. Absence of power law may indicate a more equalized distribution of influence.
Kolmogorov-Smirnov test compares the distribution above to the "ideal" power-law ones (^1, ^1.5, ^2) and looks for the best fit. If the value d is below the critical value cr it is a sign that the both distributions are similar.
Kolmogorov-Smirnov test compares the distribution above to the "ideal" power-law ones (^1, ^1.5, ^2) and looks for the best fit. If the value d is below the critical value cr it is a sign that the both distributions are similar.
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We provide LDA stats for comparison purposes only. It works with English-language texts at the moment. More languages are coming soon, subscribe @noduslabs to be informed.
positive: | negative: | neutral:
⤓ ? ⤓
Use the Bert AI model for English, Dutch, German, French, Spanish and Italian to get more precise results (slower). Standard model is faster, works for English only, is less precise, and is based on a fixed AFINN dictionary.
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Main Topical Groups:
please, add your data to display the stats...
+ ⤓ ? ⤓ 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 using the Force Atlas algorithm (Jacomy et al) and are given a distinct color.
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 using the Force Atlas algorithm (Jacomy et al) and are given a distinct color.
Most Influential Elements:
please, add your data to display the stats...
+ ⤓ ↻ ?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.
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:
N/A
?The network structure indicates the level of its diversity. It is based on the modularity measure (>0.4 for medium, >0.65 for high modularity, measured with Louvain (Blondel et al 2008) community detection algorithm) in combination with the measure of influence distribution (the entropy of the top nodes' distribution among the top clusters), as well as the the percentage of nodes in the top community.
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Action Advice:
N/A
Structural Gap
(ask a research question that would link these two topics):N/A
?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.
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
(less visible terms that link important topics):
N/A
?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.
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 Keywords
N/A
Evolution of Topics
(number of occurrences per text segment) ?
↻
The chart shows how the main topics and the most influential keywords evolved over time. X-axis: time period (split into 10% blocks). Y-axis: cumulative number of occurrences.
Drag the slider to see how the narrative evolved over time. Select the checkbox to recalculate the metrics at every step (slower, but more precise).
Drag the slider to see how the narrative evolved over time. Select the checkbox to recalculate the metrics at every step (slower, but more precise).
Main Topics
(according to Latent Dirichlet Allocation):loading...
⤓ ? ⤓LDA stands for Latent Dirichlet Allocation — it is a topic modelling algorithm based on calculating the maximum probability of the terms' co-occurrence in a particular text or a corpus.
We provide this data for you to be able to estimate the precision of the default InfraNodus topic modeling method based on text network analysis.
We provide this data for you to be able to estimate the precision of the default InfraNodus topic modeling method based on text network analysis.
Most Influential Words
(main topics and words according to LDA):loading...
We provide LDA stats for comparison purposes only. It works with English-language texts at the moment. More languages are coming soon, subscribe @noduslabs to be informed.
Sentiment Analysis
positive: | negative: | neutral:
⤓ ? ⤓
We analyze the sentiment of each statement to see whether it's positive, negative, or neutral. You can filter the statements by sentiment (clicking above) and see what kind of topics correlate with every mood.
The approach is based on AFINN and Emoji Sentiment Ranking
The approach is based on AFINN and Emoji Sentiment Ranking
Use the Bert AI model for English, Dutch, German, French, Spanish and Italian to get more precise results (slower). Standard model is faster, works for English only, is less precise, and is based on a fixed AFINN dictionary.
Keyword Relations Analysis:
please, select the node(s) on the graph see their connections...
+ ?
Top Relations / Bigrams
(both directions):
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).
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).