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Michel de Montaigne's "Essais" is a large collection (3 books) of many short subjective treatments of various topics published in 1580. Montaigne's stated design in writing, publishing, and revising the "Essais" over the period from approximately 1570 to 1592 was to record "some traits of [his] character and of [his] humours." The "Essais" were seen as an important work that established the essay as a recognized genre in literature. This work can be qualified as introspective philosophy.
Montaigne's "Essais" is not just a foundational work in the history of ideas; it's also a unique insight into the mind of one of the most curious and open thinkers in Western history. His observations on society, culture, and humanity are as relevant today as in the 16th century.
We are launching our 'Philosophical Minute' series with an excerpt from Book 2, 'Apology of Raimond de Sebonde'. We believe this passage serves as the perfect introduction to the series, as it refers to the profound wisdom of Socrates.
The wisest man who ever lived, when asked what he knew, replied that he knew that he knew nothing. He confirmed what is said, that the greatest part of what we know is the least of what we do not know: that is to say, even what we think we know is a small part of our ignorance.
Create a new node: Start by creating a new node and label it as "Montaigne". This node will serve as a container for the text you want to analyze.
Enter the excerpt: Input the selected text into the "Montaigne" node as a comment.
Run the Dimension Elicitor, set the General Context to "Philosophy", and input "Keywords" as the keyword for the analysis of the node comment.
Review the dimensions: Examine the dimensions or keywords returned by Hellixia. Remove any dimensions that seem redundant or irrelevant to your analysis.
Use the Embedding Generator on all remaining nodes. This tool captures and quantifies the semantics associated with the names and comments of each node.
Set the target node: Set "Montaigne" as the Target Node. The subsequent analyses and operations will focus on this node.
Run the Naive Learning algorithm.
Change node styles: Alter the style of all nodes to "Badges". This style will display the comment within each node.
Switch to Validation Mode.
Run the Arc Force analysis.
Apply the Radial Layout: While still in the Arc Force analysis tool, run the Radial Layout. This layout arranges the nodes in a clockwise manner according to the strength of their relationships with the target node.
Show the Arc Comments: These comments will provide information about the strength of the relationships between nodes.
Create a new node titled "Montaigne" that will contain the text we want to analyze.
Enter the excerpt as a comment within the "Montaigne" node.
Use the Dimension Elicitor with the General Context set to "Philosophy" and the keywords (Dimensions, Ideas, Themes, and Theses) to analyze the comment within your node. Review the dimensions that Hellixia returns, and remove any that appear to be redundant or irrelevant.
Apply the Embedding Generator to all remaining nodes, capturing the semantics related to their names and comments.
Exclude the "Montaigne" node.
Use the Maximum Weight Spanning Tree algorithm to create a semantic network that describes the analyzed text.
Change all node styles to Badges so that the comment within each node is displayed.
Apply the Dynamic Grid Layout to arrange the nodes.
Switch to Validation Mode.
Since the graph we're creating doesn't represent causal relationships, select the Skeleton View to remove any arc orientations.
Switch back to Modeling Mode.
Exclude the "Montaigne" node.
Change all node styles to the Discs format.
Enter Validation Mode.
Use the Symmetric Layout.
Analyze the Node Force.
Run Variable Clustering.
Open the Class Editor and utilize the Class Description Generator to assign meaningful names to the three factors you're dealing with.
Save these descriptions using the Export Descriptions feature.
Switch back to Modeling Mode.
Execute Multiple Clustering to create latent variables.
Run Taboo, enabling the option Delete Unfixed Arcs, to create a hierarchical network.
Rename the latent variables you've just created by using the previously exported descriptions as a dictionary for naming the node names.
Switch to Validation Mode.
Utilize the Node Force function.
In this second installment, we delve into a profound examination of yet another passage from Montaigne's work, 'Essais', the 'Liars' (Book I).
This is a formidable challenge for Hellixia, given that it relies on a translation from Old French.
When they disguise and change, when they are often put back on the same story, it is difficult for them not to make mistakes, because the thing as it is, having lodged itself first in memory and having been imprinted there by way of knowledge and science, it is difficult for it not to be represented in the imagination by dislodging the falsehood, which cannot have as firm and steady a foothold, and for the circumstances of the first learning not to cause the memory of the added, false or bastardized pieces to be lost. In what they invent completely, because there is no contrary impression that contradicts their falsehood, they seem to have all the less to fear to make mistakes. However, this fiction, because it is a vain and ungraspable body, readily escapes memory if it is not well secured. If, like truth, lies had only one face, we would be in a better position, for we would take the opposite of what the liar said as certain. But the reverse of truth has a hundred thousand faces and an indefinite field. The Pythagoreans posit that good is certain and finite, evil infinite and uncertain. A thousand roads deviate from the goal, only one leads to it.
This post is also linked to a discussion we had at Marcello Di Bello's presentation, "Cross-Examination with Bayesian Networks" (BayesiaLab Conference, 2022).
Create a new node: Start by creating a new node and label it as "Montaigne". This node will serve as a container for the text you want to analyze.
Enter the excerpt: Input the selected text into the "Montaigne" node as a comment.
Run the Dimension Elicitor, set the General Context to "Philosophy", and input "Keywords" as the keyword for the analysis of the node comment.
Review the dimensions: Examine the dimensions or keywords returned by Hellixia. Remove any dimensions that seem redundant or irrelevant to your analysis.
Use the Embedding Generator on all remaining nodes. This tool captures and quantifies the semantics associated with the names and comments of each node.
Set the target node: Set "Montaigne" as the Target Node. The subsequent analyses and operations will focus on this node.
Run the Naive Learning algorithm.
Change node styles: Alter the style of all nodes to "Badges". This style will display the comment within each node.
Switch to Validation Mode.
Run the Arc Force analysis.
Apply the Radial Layout: While still in the Arc Force analysis tool, run the Radial Layout. This layout arranges the nodes in a clockwise manner according to the strength of their relationships with the target node.
Show the Arc Comments: These comments will provide information about the strength of the relationships between nodes.
Copy the "Montaigne" node: Begin by copying the node titled "Montaigne".
Paste the node into a new graph: Create a new graph and paste the copied "Montaigne" node into it.
Run the Dimension Elicitor using the following keywords to guide the analysis of the node: Contents, Ideas, Milestones, Rules, Themes, Theses, and the General Context set to "Philosophy".
Review the returned dimensions: Examine the dimensions provided by Hellixia. Remove any dimensions that appear redundant or irrelevant to your analysis.
Exclude the "Montaigne" node.
Use the Embedding Generator on all remaining nodes. This will help capture the semantic associations of their names and comments.
Create a semantic network: Use the Maximum Weight Spanning Tree algorithm to form a semantic network from the analyzed text.
Change node styles to "Badges". This style will allow the comment within each node to be shown.
Apply the Dynamic Grid Layout: Use this layout option to organize the nodes on your graph. Note that this layout algorithm is not deterministic, meaning it doesn't always produce the same results given the same input. It randomly favors vertical, horizontal, or mixed orientations. Run this layout multiple times until you find a layout that best suits your preferences.
Switch to Validation Mode.
Select Skeleton View: Since the network you're generating does not represent causal relationships, choose the Skeleton View. This will remove the arc orientations, leaving only connections between nodes without indicating a direction.
Switch back to Modeling Mode.
Change node styles to Discs.
Symmetric Layout.
Enter Validation Mode.
Analyze Node Force.
Run Variable Clustering: This will identify and group similar variables based on their semantics.
Open the Class Editor.
Within the Class Editor, activate the Class Description Generator. Use it to create meaningful names for the factors you're working with.
Save the descriptions you've just created using the Export Descriptions feature.
Switch back to Modeling Mode.
Execute Multiple Clustering to create latent variables.
Next, execute the structural learning algorithm Taboo. Make sure to enable the option "Delete Unfixed Arcs." This should result in the creation of a hierarchical network.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation Mode.
Use Node Force.
The third episode of our Philosophical Minute post is about the famous philosophical statement by René Descartes, Cogito Ergo Sum, "I think, therefore I am." This statement is at the core of Western philosophy and is the starting point of Descartes' philosophical methodology, the foundational element of his metaphysics.
Descartes sought a fundamental element that could be beyond any doubt as a basis for all knowledge. He posited that the very act of doubting one's own existence served as proof of the reality of one's own mind. In essence, if one is questioning, then one must exist to be able to do so.
Considering that all the same thoughts that we have while awake can also come to us when we sleep, without any of them being true at that time, I resolved to pretend that all the things that had ever entered my mind were no more true than the illusions of my dreams.
But immediately afterwards, I noticed that while I wanted to think that everything was false, it was necessary that I, who was thinking, be something; and realizing that this truth, I think, therefore I am, was so firm and so certain that even the most extravagant suppositions of skeptics were not capable of shaking it, I judged that I could accept it without hesitation as the first principle of the philosophy I was seeking.
Start by creating a new node. Label this node "Descartes".
Input the chosen excerpt of text into the comment section of the "Descartes" node.
Run the Dimension Elicitor, set the General Context to "Philosophy", and input "Keywords" as the keyword for the analysis of the node comment.
Examine the dimensions or keywords that Hellixia has identified. Any dimensions that appear irrelevant or redundant should be removed from your analysis.
Use the Embedding Generator on all remaining nodes. This tool will quantify the semantics associated with the names and comments of each node.
Set the "Descartes" node as your Target Node.
Run the Naive Learning algorithm.
Update the visual style of all nodes to appear as "Badges". This will allow the comments within each node to be displayed.
Switch to Validation Mode.
Run an Arc Force analysis.
Use the Radial Layout while you are still within the Arc Force analysis tool. This will arrange the nodes in a clockwise fashion based on the strength of their relationships with the target node.
Show the Arc Comments to visualize information regarding the strength of the relationships between the nodes.
Start by copying the node "Descartes." Then, create a new graph and paste the node.
Utilize the Dimension Elicitor with the subsequent keywords: Arguments, Contents, Matters, Milestones, Rules, Themes, Theses, Topics, and the General Context set to "Philosophy."
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to your analysis. Next, disregard the "Descartes" node and run the Embedding Generator on all remaining nodes to apprehend the semantic associations of their names and comments.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network from the excerpt.
Change node styles to Badges to ensure each node's comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on your graph; note that this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with your taste.
Switch over to Validation Mode and select Skeleton View. Since your network doesn't represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Return to Modeling Mode and alter the node styles to Discs.
Use the Symmetric Layout and switch to Validation Mode to run a Node Force analysis.
Execute Variable Clustering: This operation will categorize analogous variables based on their semantic relationships.
Open the Class Editor and run Class Description Generator to generate descriptive names for the factors in question. Use the Export Descriptions function, and save the newly created descriptions.
Return to Modeling Mode and run Multiple Clustering to generate latent variables.
Run the structural learning algorithm Taboo. Ensure the "Delete Unfixed Arcs" option is enabled.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation and apply Node Force.
Blaise Pascal's "Pensées" (which translates to "Thoughts" in English) is a collection of fragments on theology and philosophy. Pascal, a French mathematician, physicist, and religious philosopher, began writing "Pensées" as a defense of the Christian religion, but he died before he could complete the work. The fragments he left behind were posthumously assembled and published in 1670.
This Philosophical Minute centers around a passage from Pensées, which delves into the human propensity to neglect the present moment, habitually yearning for the future, or dwelling on the past.
We never care about the present. We anticipate the future as too slow to come, as if to hasten its course; or we recall the past to stop it as too quick: so careless, we wander in times that are not ours, and do not think of the only one that belongs to us; and so vain, we think of those that are nothing anymore, and let slip without reflection the only one that remains.
It is because the present, usually, hurts us. We hide it from our sight, because it afflicts us; and if it is pleasant to us, we regret seeing it slip away.
The present is never our end: the past and the present are our means; the only future is our end. Thus we never live, but we hope to live; and, always preparing to be happy, it is inevitable that we never are.
Create a new node: Start by generating a new node named "Blaise Pascal - Pensées". This node will hold the text that you plan to analyze.
Insert the text: Add the selected excerpt into the comment section of the "Blaise Pascal - Pensées" node.
Run the Dimension Elicitor, set the General Context to "Philosophy", and input "Keywords" as the keyword for the analysis of the node comment.
Assess the extracted dimensions: Evaluate the keywords or dimensions identified by Hellixia and eliminate any that are redundant or irrelevant.
Use the Embedding Generator for all remaining nodes. This tool will distill the semantics of the names and comments of each node into a quantifiable form.
Set "Blaise Pascal - Pensées" as the Target Node.
Run the Naive Learning algorithm.
Change the style of all nodes to "Badges". This style will display the comment embedded within each node.
Switch to Validation Mode.
Perform an Arc Force analysis.
While within the Arc Force analysis tool, run the Radial Layout. This will arrange the nodes in a clockwise pattern in relation to their connection strength with the target node.
Show the Arc Comments, which will provide information about the strength of the relationships between nodes.
Start by making a copy of the node named "Blaise Pascal - Pensées".
Open a new graph and paste the copied "Blaise Pascal - Pensées" node.
Use the following keywords to guide the Dimension Elicitor in its analysis of the node: Arguments, Matters, Milestones, Rules, Themes, Theses, Topics, and the General Context set to "Philosophy".
Inspect the dimensions suggested by Hellixia. Any dimensions that are irrelevant or redundant should be removed from your analysis.
Exclude the "Blaise Pascal - Pensées" node.
Use the Embedding Generator on all remaining nodes.
Run the Maximum Weight Spanning Tree algorithm to create a semantic network based on the text analysis.
Change the style of all nodes to "Badges". This will display the comment within each node.
Run the Dynamic Grid Layout to organize the nodes on your graph. Note that this algorithm's output is not deterministic; it may favor vertical, horizontal, or mixed orientations. Execute this layout multiple times until you find the most suitable arrangement.
Switch to Validation Mode.
As the graph you are building does not represent causal relationships, opt for the Skeleton View. This will remove all arc directions, leaving only the node connections without any specified direction.
Switch back to Modeling Mode.
Change all node styles to Discs.
Use the Symmetric Layout to organize your nodes in the graph.
Go to Validation Mode.
Conduct a Node Force analysis to evaluate the strength of associations in your graph.
Execute Variable Clustering: This operation will categorize analogous variables based on their semantic relationships.
Open the Class Editor.
Run Class Description Generator: Use this function to generate descriptive names for your identified factors. This helps to make the output more understandable and interpretable.
Save these descriptions by using the Export Descriptions function.
Switch back to Modeling Mode.
Run Multiple Clustering.
Run the Taboo algorithm: Use this structural learning algorithm to learn a hierarchical network. Make sure to enable the "Delete Unfixed Arcs" option to remove unnecessary connections and streamline your model.
Use the descriptions you exported earlier as a dictionary to rename the latent variables you've just created. This helps in making your model more understandable and keeps the nodes' names consistent with their semantic meaning.
Switch to Validation Mode.
Apply Node Force.
In this fifth episode, we delve into another passage from Blaise Pascal's Pensées. This particular segment sheds light on the compromise required to uphold societal harmony, a state considered the highest form of good.
Without doubt, the equality of goods is just; but, unable to make it force to obey justice, we have made it just to obey force; unable to strengthen justice, force was justified, so that the just and the strong might be together, and peace might be, which is the sovereign good.
Create a new node: Start by generating a new node named "Blaise Pascal - Pensées". This node will hold the text that you plan to analyze.
Insert the text: Add the selected excerpt into the comment section of the "Blaise Pascal - Pensées" node.
Run the Dimension Elicitor, set the General Context to "Philosophy", and input "Keywords" as the keyword for the analysis of the node comment.
Assess the extracted dimensions: Evaluate the keywords or dimensions identified by Hellixia and eliminate any that are redundant or irrelevant.
Use the Embedding Generator for all remaining nodes. This tool will distill the semantics of the names and comments of each node into a quantifiable form.
Set "Blaise Pascal - Pensées" as the Target Node.
Run the Naive Learning algorithm.
Change the style of all nodes to "Badges". This style will display the comment embedded within each node.
Switch to Validation Mode.
Perform an Arc Force analysis.
While within the Arc Force analysis tool, run the Radial Layout. This will arrange the nodes in a clockwise pattern in relation to their connection strength with the target node.
Show the Arc Comments, which will provide information about the strength of the relationships between nodes.
Start by making a copy of the node named "Blaise Pascal - Pensées".
Open a new graph and paste the copied "Blaise Pascal - Pensées" node.
Use the following keywords to guide the Dimension Elicitor in its analysis of the node: Arguments, Contents, Ideas, Matters, Milestones, Motifs, Rules, Themes, Theses, Topics, and the General Context set to "Philosophy".
Inspect the dimensions suggested by Hellixia. Any dimensions that are irrelevant or redundant should be removed from your analysis.
Exclude the "Blaise Pascal - Pensées" node.
Use the Embedding Generator on all remaining nodes.
Run the Maximum Weight Spanning Tree algorithm to create a semantic network based on the text analysis.
Change the style of all nodes to "Badges". This will display the comment within each node.
Run the Dynamic Grid Layout to organize the nodes on your graph. Note that this algorithm's output is not deterministic; it may favor vertical, horizontal, or mixed orientations. Execute this layout multiple times until you find the most suitable arrangement.
Switch to Validation Mode.
As the graph you are building does not represent causal relationships, opt for the Skeleton View. This will remove all arc directions, leaving only the node connections without any specified direction.
Switch back to Modeling Mode.
Change all node styles to Discs.
Use the Symmetric Layout to organize your nodes in the graph.
Go to Validation Mode.
Conduct a Node Force analysis to evaluate the strength of associations in your graph.
Execute Variable Clustering: This operation will categorize analogous variables based on their semantic relationships.
Open the Class Editor.
Run Class Description Generator: Use this function to generate descriptive names for your identified factors. This helps to make the output more understandable and interpretable.
Save these descriptions by using the Export Descriptions function.
Switch back to Modeling Mode.
Run Multiple Clustering.
Run the Taboo algorithm: Use this structural learning algorithm to learn a hierarchical network. Make sure to enable the "Delete Unfixed Arcs" option to remove unnecessary connections and streamline your model.
Use the descriptions you exported earlier as a dictionary to rename the latent variables you've just created. This helps in making your model more understandable and keeps the nodes' names consistent with their semantic meaning.
Switch to Validation Mode.
Apply Node Force.
Baruch Spinoza's "Ethics" (often referred to as "Ethica" from its Latin title "Ethica, ordine geometrico demonstrata", meaning "Ethics Demonstrated in Geometrical Order") is a philosophical treatise written in the mid-17th century. It is one of the most significant and controversial works of the Enlightenment, and it presents Spinoza's metaphysical, epistemological, moral, and political views.
The structure of "Ethics" is unique: it is laid out like a geometrical treatise, akin to Euclid's "Elements". Starting with definitions and axioms, Spinoza proceeds with propositions, proofs, corollaries, and scholia (notes), aiming to demonstrate his philosophy with mathematical precision.
In this particular semantic analysis, we explore one of the famous quotes from Ethics:
Desire is the very essence of man, insofar as it is conceived as determined to some action by any of its affections.
Start by creating a new node. Label this node "Spinoza".
Input the chosen excerpt of text into the comment section of the "Spinoza" node.
Use the keyword "Keywords" to guide the Dimension Elicitor in analyzing the comment in the "Spinoza" node. Specify the General Context for your analysis as "Philosophy". By setting this context, you are providing direction for the Dimension Elicitor to understand the broader topic of your text. The Dimension Elicitor will then identify and extract relevant dimensions or keywords from the comment.
Examine the dimensions or keywords that Hellixia has identified. Any dimensions that appear irrelevant or redundant should be removed from your analysis.
Use the Embedding Generator on all remaining nodes. This tool will quantify the semantics associated with the names and comments of each node.
Set the "Spinoza" node as your Target Node.
Run the Naive Learning algorithm.
Update the visual style of all nodes to appear as "Badges". This will allow the comments within each node to be displayed.
Switch to Validation Mode.
Run an Arc Force analysis.
Use the Radial Layout while you are still within the Arc Force analysis tool. This will arrange the nodes in a clockwise fashion based on the strength of their relationships with the target node.
Show the Arc Comments to visualize information regarding the strength of the relationships between the nodes.
Start by copying the node "Spinoza". Then, create a new graph and paste the node.
Utilize the Dimension Elicitor with the subsequent keywords: Ideas, Rules, Themes, Theses, Topics, and the General Context set to "Philosophy".
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to your analysis. Next, exclude the "Spinoza" node and run the Embedding Generator on all remaining nodes to apprehend the semantic associations of their names and comments.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network from the excerpt.
Change node styles to Badges to ensure each node's comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on your graph; bear in mind that this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with your taste.
Switch over to Validation Mode and select Skeleton View. Since your network doesn't represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Return to Modeling Mode and alter the node styles to Discs.
Use the Symmetric Layout and switch to Validation Mode to run a Node Force analysis.
Execute Variable Clustering: This operation will categorize analogous variables based on their semantic relationships.
Open the Class Editor and run Class Description Generator to generate descriptive names for the factors in question. Use the Export Descriptions function, and save the newly created descriptions.
Return to Modeling Mode and run Multiple Clustering to generate latent variables.
Run the structural learning algorithm Taboo. Ensure the "Delete Unfixed Arcs" option is enabled.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation and apply Node Force.
All men are born in ignorance of causes, and a universal appetite of which they are conscious drives them to seek what is useful to them.
A first consequence of this principle is that men believe they are free, because they are conscious of their volitions and desires, and do not think at all about the causes that predispose them to desire and to want.
The result, secondly, is that men always act with an end in mind, namely, their own utility, the natural object of their desire.
The supreme end of man, guided by reason, his supreme desire, this desire by which he strives to regulate all others, is therefore the desire that drives him to adequately understand both himself and all things that fall within his comprehension.
Start by creating a new node. Label this node "Spinoza".
Input the chosen excerpt of text into the comment section of the "Spinoza" node.
Use the keyword "Keywords" to guide the Dimension Elicitor in analyzing the comment in the "Spinoza" node. Specify the General Context for your analysis as "Philosophy". By setting this context, you are providing direction for the Dimension Elicitor to understand the broader topic of your text. The Dimension Elicitor will then identify and extract relevant dimensions or keywords from the comment.
Examine the dimensions or keywords that Hellixia has identified. Any dimensions that appear irrelevant or redundant should be removed from your analysis.
Use the Embedding Generator on all remaining nodes. This tool will quantify the semantics associated with the names and comments of each node.
Set the "Spinoza" node as your Target Node.
Run the Naive Learning algorithm.
Update the visual style of all nodes to appear as "Badges". This will allow the comments within each node to be displayed.
Switch to Validation Mode.
Run an Arc Force analysis.
Use the Radial Layout while you are still within the Arc Force analysis tool. This will arrange the nodes in a clockwise fashion based on the strength of their relationships with the target node.
Show the Arc Comments to visualize information regarding the strength of the relationships between the nodes.
Start by copying the node "Spinoza". Then, create a new graph and paste the node.
Utilize the Dimension Elicitor with the subsequent keywords: Arguments, Ideas, Matters, Milestones, Motifs, Rules, Themes, and the General Context set to "Philosophy".
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to your analysis. Next, exclude the "Spinoza" node and run the Embedding Generator on all remaining nodes to apprehend the semantic associations of their names and comments.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network from the excerpt.
Change node styles to Badges to ensure each node's comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on your graph; bear in mind that this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with your taste.
Switch over to Validation Mode and select Skeleton View. Since your network doesn't represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Return to Modeling Mode and alter the node styles to Discs.
Use the Symmetric Layout and switch to Validation Mode to run a Node Force analysis.
Continuing with Baruch Spinoza's Ethics (see ), we focus on another passage about desire, determinism, and perceived free will in human actions.:
Welcome to the eighth installment of the Philosophical Minute, where we continue our exploration of the works of Baruch Spinoza. Today's focus is a captivating foray into Spinoza's reflections on desire, and its profound influence on our perceptions of good and evil:
We consider good the thing that we desire; and consequently, we call the thing that inspires us with aversion, bad; so that everyone judges according to their passions what is good or bad, what is better or worse, what is most excellent or most contemptible.
Spinoza, in his meticulous examination, sheds light on the intrinsic nature of desire and its pivotal role in shaping human behavior and ethics. How does what we desire dictate our moral compass? Why do we perceive certain desires as virtuous and others as vice? Spinoza's insights into these questions offer a deep dive into the undercurrents of human psychology and the constructs of morality.
Node Creation: Start by generating a new node. Name it "Spinoza".
Text Inclusion: Insert your chosen text excerpt into the comment section of this "Spinoza" node.
Dimension Elicitation: Use the Dimension Elicitor with the keyword "Keywords" to analyze the comment within the "Spinoza" node. Define the General Context as "Philosophy". This context directs the elicitor to frame the analysis within the broader realm of philosophical discourse.
Dimension Review: Evaluate the dimensions or keywords identified by Hellixia. Remove any that seem redundant or not pertinent to your objective.
Semantic Quantification: Run the Embedding Generator for all the nodes that are still in play. This process translates the semantic elements of each node's name and comments into quantifiable metrics.
Target Node Designation: Designate "Spinoza" as your primary or target node.
Learning Algorithm: Launch the Naive Learning algorithm.
Visualization: Alter the visual representation of every node to the "Badges" style. It ensures that the comments associated with each node are directly visible.
Validation: Transition your workspace to the Validation Mode.
Arc Analysis: Run the Arc Force analysis.
Graph Layout: While still in the Arc Force analysis tool, run the Radial Layout. This method organizes nodes in a circle around your target node, positioning them based on the strength of their connection to the target.
Arc Visualization: Activate the Arc Comments. This feature superimposes a visualization layer on your network, displaying information about the arcs' strengths.
Start by copying the node "Spinoza". Then, create a new graph and paste the node.
Utilize the Dimension Elicitor with the subsequent keywords: Arguments, Ideas, Matters, Milestones, Motifs, Rules, Themes, and the General Context set to "Philosophy".
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to your analysis. Next, exclude the "Spinoza" node and run the Embedding Generator on all remaining nodes to apprehend the semantic associations of their names and comments.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network from the excerpt.
Change node styles to Badges to ensure each node's comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on your graph; bear in mind that this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with your taste.
Switch over to Validation Mode and select Skeleton View. Since your network doesn't represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Switch back to Modeling Mode and change the visual representation of each node to the "Discs" style. The disc style offers a clean and straightforward visual, which might be easier to interpret in some contexts compared to the badge style.
Use the Symmetric Layout tool.
Switch to Validation Mode and run the Node Force analysis.
Carry out Variable Clustering: This action will group similar variables together based on their semantic connections.
Open the Class Editor and run Class Description Generator to generate descriptive names for the factors in question. Use the Export Descriptions function, and save the newly created descriptions.
Switch back to Modeling Mode and run Multiple Clustering to produce latent variables.
Launch the structural learning algorithm Taboo. Ensure the "Delete Unfixed Arcs" option is enabled.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation and run Node Force.