To Be or not to Be is a question that has been asked many times before. We were curious what happens if we visualize this passage of Shakespeare’s classics as a text network (using the polysingularity method) and then read the text through the network. What are the other possibilities contained within the passage?
Here is the text network visualization (click to enlarge):
Shakespeare's Hamlet text network visualization
Here’s the original text:
To be, or not to be: that is the question:
Whether ’tis nobler in the mind to suffer
The slings and arrows of outrageous fortune,
Or to take arms against a sea of troubles,
And by opposing end them? To die: to sleep;
No more; and by a sleep to say we end
The heart-ache and the thousand natural shocks
That flesh is heir to, ’tis a consummation
Devoutly to be wish’d. To die, to sleep;
To sleep: perchance to dream: ay, there’s the rub;
For in that sleep of death what dreams may come
When we have shuffled off this mortal coil,
Must give us pause: there’s the respect
That makes calamity of so long life;
For who would bear the whips and scorns of time,
The oppressor’s wrong, the proud man’s contumely,
The pangs of despised love, the law’s delay,
The insolence of office and the spurns
That patient merit of the unworthy takes,
When he himself might his quietus make
With a bare bodkin? who would fardels bear,
To grunt and sweat under a weary life,
But that the dread of something after death,
The undiscover’d country from whose bourn
No traveller returns, puzzles the will
And makes us rather bear those ills we have
Than fly to others that we know not of?
Thus conscience does make cowards of us all;
And thus the native hue of resolution
Is sicklied o’er with the pale cast of thought,
And enterprises of great pith and moment
With this regard their currents turn awry,
And lose the name of action.–Soft you now!
The fair Ophelia! Nymph, in thy orisons
Be all my sins remember’d.
During our reading we tried to follow the nodes in a more or less random way to ensure that the resulting trajectory would only be subject to the possibilities exercised within the text’s network structure, not influenced by our own idea of how the words are semantically linked. The result was as following:
Make coward native resolution.
Sickly pale thinks the enterprise of the moment.
Regard the awry current.
Turn, regard, turn moment.
Awry soft fear horizon.
Be sleep death dread and life to bear.
Scorn time whip time oppressor
Proud of wrong man contumely
The pang and love despise the law
Delay insolence, spurn the patient
Unworthy merit takes the quiet.
Make life dread death
Sleep death dread life
Make coward native resolution
Hue native coward make
Life dread death sleep be remember
The sins to be devoutly consummation
Flesh heir flesh shock
A thousand heartache thousand shocks
Flesh and thousand shock and flesh
And consummation devoutly sleep
Oppose the trouble arm the fortune
The arrow sling the fortune arrow
The outrageous sling suffers nobler mind
Suffer mind nobler mind to suffer sling the fortune
The arm and trouble oppose the sleep
Death, dread life, make life
Bear time oppressor wrong and proud
We leave it up to you to judge our efforts in re-reading Shakespeare’s poetry – it would be actually interesting to hear what you think about it and whether you find it somewhat related to the original text.
A brief visual analysis of the resulting network image for the text above shows that structurally it’s not too far away from the original passage. The most central concepts are similar and structurally they are connected to the similar clusters of meaning circulation. However, some concepts within these clusters. are much more emphasized than in the original text: especially the ones on the periphery, such as “orizon”, “sickly”, “fortune”, “flesh”, and “patient”.
Hamlet read randomly through a text network
Brief data analysis also shows that the quantitive parameters of the resulting graph are similar, the only difference is that we used a smaller vocabulary during the reading and that resulted in shorter diameter and average path length (as less words are needed to connect one morpheme to another). However, the average degree and modularity are somewhat similar and both graph have very similar degree distribution charts and overall graph structure.
nodes: 65 vs 107 in the original
edges: 157 vs 209
av degree: 4.8 vs 3.9
diameter: 9 vs 16
av path: 4.4 vs 6.7
modularity: 0.7 vs 0.778
We will be reporting more results from this work later. In the meanwhile, the original raw data for you to play around are available in GEXF format here (right-click to save, then open with Gephi or GexfWalker).