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Explore the main topics and terms outlined above or see them in the excerpts from this text below.
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I had a dream, which was not all a dream. The bright sun was extinguish'd, and the stars Did wander darkling in the eternal space, Rayless, and pathless, and the icy earth Swung blind and blackening in the moonless air; Morn came and went—and came, and brought no day, And men forgot their passions in the dread Of this their desolation; and all hearts Were chill'd into a selfish prayer for light: And they did live by watchfires—and the thrones, The palaces of crowned kings—the huts, The

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habitations of all things which dwell, Were burnt for beacons; cities were consum'd, And men were gather'd round their blazing homes To look once more into each other's face; Happy were those who dwelt within the eye Of the volcanos, and their mountain-torch: A fearful hope was all the world contain'd; Forests were set on fire—but hour by hour They fell and faded—and the crackling trunks Extinguish'd with a crash—and all was black. The brows of men by the despairing light Wore an unearthly

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aspect, as by fits The flashes fell upon them; some lay down And hid their eyes and wept; and some did rest Their chins upon their clenched hands, and smil'd; And others hurried to and fro, and fed Their funeral piles with fuel, and look'd up With mad disquietude on the dull sky, The pall of a past world; and then again With curses cast them down upon the dust, And gnash'd their teeth and howl'd: the wild birds shriek'd And, terrified, did flutter on the ground, And flap their useless wings;

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the wildest brutes Came tame and tremulous; and vipers crawl'd And twin'd themselves among the multitude, Hissing, but stingless—they were slain for food. And War, which for a moment was no more, Did glut himself again: a meal was bought With blood, and each sate sullenly apart Gorging himself in gloom: no love was left; All earth was but one thought—and that was death Immediate and inglorious; and the pang Of famine fed upon all entrails—men Died, and their bones were tombless as their flesh;

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The meagre by the meagre were devour'd, Even dogs assail'd their masters, all save one, And he was faithful to a corse, and kept The birds and beasts and famish'd men at bay, Till hunger clung them, or the dropping dead Lur'd their lank jaws; himself sought out no food, But with a piteous and perpetual moan, And a quick desolate cry, licking the hand Which answer'd not with a caress—he died. The crowd was famish'd by degrees; but two Of an enormous city did survive, And they were enemies: they

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met beside The dying embers of an altar-place Where had been heap'd a mass of holy things For an unholy usage; they rak'd up, And shivering scrap'd with their cold skeleton hands The feeble ashes, and their feeble breath Blew for a little life, and made a flame Which was a mockery; then they lifted up Their eyes as it grew lighter, and beheld Each other's aspects—saw, and shriek'd, and died— Even of their mutual hideousness they died, Unknowing who he was upon whose brow Famine had written

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Fiend. The world was void, The populous and the powerful was a lump, Seasonless, herbless, treeless, manless, lifeless— A lump of death—a chaos of hard clay. The rivers, lakes and ocean all stood still, And nothing stirr'd within their silent depths; Ships sailorless lay rotting on the sea, And their masts fell down piecemeal: as they dropp'd They slept on the abyss without a surge— The waves were dead; the tides were in their grave, The moon, their mistress, had expir'd before; The winds were

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wither'd in the stagnant air, And the clouds perish'd; Darkness had no need Of aid from them—She was the Universe.

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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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    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.

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    Structural Gap
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    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
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    ⤓ 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):

     
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