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• 1 media/RELBuildingBackground.jpg 2022-11-01T14:15:30-07:00 Platonic Dialog Meets Digital Humanities 19 plain 2022-12-07T18:33:15-08:00

  
  Brief Abstract

  Plato was one of the most influential figures in history, especially in the field of philosophy and political science. This project brings together one of his major works, The Republic, with a growing field in academia and scholarships in general, Digital Humanities. The potential conclusions (whether they are reasonable or not) one could draw by analyzing the Republic are many, so one of the goals of this project is to analyze a few of them. To do this, the project will present the manner in which the text data was collected through Voyant-Tools. The goal was to analyze all ten books that compose that compose the Republic through the lens of which words were most often said and the words that particular word was associated with. First, I will explain how Voyant-Tools analyze texts and give some examples of words they automatically omit. Then, I will list, then explain, the words I omitted personally. Next, I will delve into each book and explain my results, tying in different books when necessary. There will be several visualizations from Voyant-Tools that will enable the reader to have a better understanding of the literary analysis. After that I will extrapolate potential conclusions which can be made regarding the results of my project. Finally, I will wrap up with how these conclusions must not be taken too far because of the issues one sees now within data collection and analysis. 
   

  Essay: Platonic Textual Analysis

  To begin, I will explain Voyant-Tools and how this project utilized this site. Voyant is a website one can use, free of charge, to analyze small or large bodies with different features and visualizations to use in order to express the data the way the creator desires. When one copies the text into the designated bar, it is a matter of seconds before the site takes you to a different page where it has analyzed and visualized the text. The program simply counts the number of words it was given, and analyzes which words came up the most; however, omitting words like, “a” “and” “the”, as well as single letters. This is because if text analysis did not omit words like these, then the program would not be telling the viewer anything he or she did not already know. I included a few extra words to the omission list because of the way Plato wrote his dialogues (or the way the translators translated his work). Dialogs will, in general, have many words like “said” “say” “reply” etc. so I decided to omit each of those words since each one of my analyses did not produce anything new. The list is as follows: say, said, replied, reply. 

  On to the texts and visualizations itself, it was really interesting to see what was said and how many times, though this project is only concerned with the most frequently used word from each book and the words frequently associated with it in the text. I will explain and analyze each book in order, beginning with Book one. The most frequently used word in the book one is, good:
  

  This is very insightful, and a lot can be done with this visualization. What this image shows is the most frequently used word in blue with lines connecting the words highly associated with it in orange boxes, with the thickness of the line determining the frequency that the words were associated with each other. For example, one could conclude that Plato is talking about the good, and when he talks about good, it is frequently paired with the words: evil, just, wise and friends. There is a narrative that can be created from this data (as well as all ten books as this project will show) which might be molded by the scholar or person who is interested in this text because they can “infer” what Book one is about through this text analysis. One could say that Plato is primarily talking about “the good” in this book and he is contrasting it with evil and explaining that the just and the wise are highly correlated to the good, which lays down a basis for a moral code. Notice what I did there? Many times, during the collection of data, the ones collecting it infer meaning and intent. They then say that “the data shows...” or “the data says...” as if the data produced this coherent (usually quite useful) narrative which is devoid of bias. 

  Let's look at the next book to add on top of the narrative that has already been created: 
  
  First, I will explain exactly what is going on here. The most frequently used word in Book two is justice, but one of the words that was highly associated with it was the word that was the most used in Book one, good. I decided to track if a word, which was previously the most common in any of the prior books, was highly associated with the most frequently used word in the given book I made a visualization for. In this case, I gathered the most associated words with not only “justice”, but “good”. The word justice has the following words associated with it: origin, nature, injustice (which is highly correlated), sake and good. For good, the highly correlated words are: god, justice, evil, and guardian. Going back to the narrative, when one sees the correlation between justice and good, this may present an opportunity to further their interpretation. Plato can be seen as continuing his moral code or philosophy by pointing to the data and showing that their narrative is “backed up by the data” or comes “straight from the data”. To extrapolate further from the data, one could say that in Book two, Plato is talking about nature and origin of justice and how it relates to the concept of good. 
  
  Thus far, this project’s goal has been to demonstrate that the data which I have abstracted must have a framework to understand what one is looking at. I created my own brief and shallow (to save time) interpretation of the data to give an actual example of what it is like for one's own framework to shape the data which is at hand. While being able to collect and observe data, especially large amounts of it, is very interesting, the ability to draw conclusions about the meaning of the data seems to impose a framework which is not there. To continue, let's take a look now at the graph of each most frequently used word from all ten books in the Republic:
  
  This will make a really nice example of an interpretation of a text from a bird's eye view. Notice that the the graph's top word is "state" in Book four, which shows up a grand total of 84 times, followed by "good" in book one at 78 times, then coming in at third is "true" form Book seven at 62. One can also see that out of the ten, only 2 and a half (because of the tie in Book ten) of the words which are most frequently used do not show up more than once at the top. Without imposing a structure of interpretation onto the data itself, one can reasonably conclude that Plato does talk about the words, "state, good, and true". One cannot, however, extrapolate further than that without making assumptions based on bias, personal framework, or ideological motivation. The reason is because one does not know what Plato meant when he wrote "good" in each particular spot that he did, according to the person who is translating the work. On the other hand, this does not mean that all hope is lost in that regard to the ability to understand the meaning of texts, and the data which is extracted from it. One must analyze each of the words of interest in the context of the sentence they came from to gain an understanding of the words use. It does not stop there though, More people have to do the same thing and bring each person's understanding together to get a fuller and broad view of the words. Take this example from Book seven:
  If one is interested in gaining an understanding of these words, one cannot simply look at the text visual and infer a meaning. This would be mapping one's own interpretation on to the text. scholars must go to the origin of the visualization and read the words in context, then put their understanding together. The issues that Digital Humanities, and this project, are trying to show that the interpretation of data is just that, an interpretation. One must come together collectively to understanding these texts and not proport a single interpretation as the "right" or "proper". The interpretation I gave at the beginning was an example of what many do when looking at data and visualizations of data. One must be careful not to take their understanding of data too far to preserve the credibility of academia and scholarship.