In the beginning of the semester I thought this class was something that I’ll never understand because I’m not good with working a computer. I think during this semester I learned to have a lot of patience with understanding new ways to learn how to look at a text. It wasn’t easy but I learned not to give up because after a while it started to make sense after I continued to look over my notes. I think I really enjoyed looking at network analysis because of the nodes and edges. When creating my network analysis from my collection of written texts, I’ve realized that I had a lot of nodes that carried a lot of weight compared to the other nodes. A lot of what we learned is going to stick with me because it traumatized me in a good way. Now I actually want to learn more about the text and the author that I’m reading about. I also think R Studio was fun to use when I was using my corpus. This class was an amazing experienced and I’ll never forget it.
The project was overall frustrating because I felt the other projects I honestly learned more interesting facts from. In my Network Analysis I noticed that it is really split up into three groups. On the right what I see is Africa and Italy being close together. That was not much of a surprise due to other analysis’ Italy and Africa were always pretty much close together. Going towards the middle we see that Italy has one of the biggest circles. This means that it has most relations with other works. It has 7 edges Which is the most out of all of them. Going towards the left we pretty much see al of Canada together and a little Australia. All of the Canada circles are pretty small in comparison to the other circles on the analysis. Both Italy and Africa have larger circles than the other countries which I felt was interesting.
The network analysis shows various results. It was tested through it’s weight, its relevance towards each other and how well each story connected. Some stories share a direct connection with each other butt all in all, each story is undirected with each other. Each text has it’s own quality to it that made it special. There are also other texts that are within the same bounds of each other through style and various similarities such as themes or story line.
When observing the impact that the weight has on each story or text, we can see that it is all undirected. Each story has its connection to the weight but there is no direct correlation between the texts. What caught my attention are the lines; some lines are longer than others and some lines are shorter than others. This could mean a relative closeness between texts or a relative closeness between the weight and the correlation of the text. Network analysis allows us to see how texts correlate between each other but there is never a definite answer to what the overall diagnosis is. It is easy and simple to show that based on the picture below, each text is undirected. They have no correlation between one another.
When looking at the correlation between the source and the target, they have a single strand of communication within each node, but there is no directed or continued communication between each node and text. One example that was proven correct was the correlation between Troy and Iliad. Troy and Iliad are different stories but they are both texts from Greek mythology and some of the characters and events cross one another. Another guess as to what might bring the two together is the correlation between authors; the author might be the same as well as ideas. Beauty and the Beast, Aladdin, and Cinderella are all correlated within the same spot. I found this interesting because the idea of true love, princesses and princes are all themes and tropes of these texts. Some results baffle me such as why The Jungle Book and Peter Pan are correlated with each other towards one spot and why are they not separated.
In the beginning of this course, I thought that it was a huge mistake staying in this class. I had a lot of doubts about whether or not this class was going to benefit me and whether or not it was beneficial to my major. However, I decided to stick it through and see what I would have gotten out of it. From the beginning of the semester to now, I can honestly say that I am glad that I was able to stay and endured the learning process. Certain parts of the classes were hard to understand it was also in turn weird for me because I never had to learn how to analyze books through a computer.
I learned various things throughout this class and it was topics that I know I would have never learned elsewhere. This is a class that helps you grow as an individual because it helps you see that you are not alone and all the students surrounding you were all in the same boat as you. It forced you to interact with others and to also help each other. Communication skills and community skills were created through this class. Would I ever take a class like this again? Honestly, that is still a pending decision.
There was not an overall difficulty in this class because everything was explained in steps. The only thing that I would say that was difficult were the projects towards the end of the semester. Some projects were easy to catch up on and others took a while to get. In my opinion, it was difficult because of the various steps. But when you look at the overall picture, you needed one topic to show you the way through another project. Each project contributed to another project and that made it a little bit easier.
I would recommend this class to others because it was a change of pace and a change of direction. As English majors we are focused on writing and analyzing a book or a chapter by using other schools of thought that we sometimes forget that there are other resources that can help us. Technology has advanced and I think that as a group of individuals, we need to acknowledge that and use it to our advantage. Overall, it was a great class and I do believe that I learned a lot and I received more than I expected.
When analyzing the all the syllabi throughout various professors in 100 level courses, some of the basic words came about such as this, assignments, the, and, etc. This did not shock me because as a college student, when reading a syllabus, we see these words all the time. A syllabus is used as a tool and a reference for students to look on through out the semester in order to keep track on their school work and to understand the various assignments that are coming up. Professors use this tool as a unofficial contract between themselves and students so that both are at an understanding to what is expected of them throughout the semester.
I ran a PCA analysis on the syllabi for the 100 level classes. It is however unclear as to why these classes are so common through the PCA. I first analyzed the 1,000 most frequent words. I thought that by giving a broader spectrum to analyze, the results will be much greater in difference but that did not show to be true. Each class may be the same level and number, but when thought by a different professor who has the ability to decide the topics, the classes differ between each other. It does not matter the grade scale or whether the classes are the same level, they have different points on PCA 1&2 in the graph. The differences are made clear through the various colors on the graph. Most of the graph contains a green color, and then there is a burgundy colors that is spread out through the graph as well. One prediction or inference that I can make towards this would be that there is a chance that certain professors teach various classes and they sometimes recycle the material or books that is read during the duration of the semester. As an English major, this has been shown multiple times through the semesters.
When looking at the PCA symbols, it become easier to see which classes are most common and least common throughout the graph. It became most apparent that English 110 classes correlate more within the 100 level courses in the English department at Queens College. Through the symbols graph that is shown below, the English 110 classes has taken over all four quadrants of the graph. The question that remains is, what are these professors teaching in English 110 that is being spread throughout all the other courses? The only educated guess that I can infer is that English 110 is a core requirement that all Queens College students need to take before embarking on any other English classes. Also, it does not matter what your major is in Queens College, all students are required to take English 110. Therefore, maybe English 110 is required to make the class as diverse in topics and readings.
I downloaded R Studio to my laptop for this project. I set the working directory which was my corpus. I used stylo and began to work. I started with the cluster analysis of my corpus which consisted of Gothic literature. It looked at the first 100 frequent words of the corpus. In a few seconds I ran stylo and the visualization shows me two separate clusters. The shorter stories such as Poe’s The Masque of Red Death and The Cask of Amontillado stand out with Perkin’s the Yellow Wallpaper. I think they can even be considered outliers. I noticed that there were three texts that were separate into another cluster with no space in between. They were Bronte’s Villete, Dumas Count of Monte Cristo and Stevenson Dr. Jekyll and Mr. Hyde. This showing there must be a similarity in words between the text. I was curious to see why Godwin and Shelley’s text were similar to each in the cluster analysis. Initially, I thought it was because they talked about a mysterious creature. I researched each author and found that they are related. Shelley is Godwin’s daughter. They have a similarity in text although Caleb Williams was published years before Frankstein. The relationship between the two Bronte texts I did expect since they are sisters. The two texts written by LeFanu were also close together. Le Fanu’s text also each has female teenage protagonist while the Bronte’s text is female centric. Jane Eyre is narrated by a female protagonist but Wuthering Heights is just narrated by a woman. Plus, these four texts are considered Romantic Gothic literature. I can say that Woman in White probably is also Romantic because it closer to them.
In the Bootstrap Consensus Tree, I split the search of the most frequent words from a 100 to a 1000. The results were a bit different. The two novels by Leroux and Hugo who are French authors are clustered together. The novels Count of Monte Cristo and Villete now were closer together and set in France. While The Castle of Oranto and The Mysteries of Udolpho were now also grouped together and I found out both set in a castle and characters involved were nobles or royalty. Finally another difference was between The Legend of Sleepy Hollow and The Canterville Ghost which were closer in this visualization. They both include American families and are shorter texts. I enjoyed how colorful the visualization are and what conclusions I found because of these two types of analysis. The cluster analysis shows the most frequent words in sections of 100 or less. While the Bootstrap Consensus Tree looks at most frequent words in sections of 1000 or more. I know I can play around with these to find even more about the text.
When I signed up for this English Seminar, I didn’t quite understand what it was about but knew it was something different. I wanted to take a class that would push me out of my comfort zone. The only thing I knew about it was that it dealt with computers. I jumped at the chance to be able to incorporate technology into my learning experience. I was always very good with computers and knew that I would pick up the material rather quickly. During this class I enjoyed discovering new digital ways to analyze text. I was introduced to different types of programs which quickly analyzed large groups of texts and gave me visualizations that showed differences. It used words, group of words and different groups of text to show them. They showed me relationships and patterns that I would not have been able to figure out on my own. I like that each project assigned had us show how we can use a different technique on our corpus every week. I was happy to see the results which weren’t always different but showed me a different aspect of the text. I also spent a lot of time on the corpus as well. I did feel that maybe this class should have been longer like my theory class that is nearly two hours. Time really flew in class. I also always felt that although I understood what I was doing some of my classmates needed more time. I have to agree that if this seminar was turned into an honors seminar in which the students present a final project applying different forms of digital techniques it would be incredible. Especially because it is not a subject that most English majors know about and would produce more insight into the field.
This class helped me see that there is more to text then what I can see in it. I feel like I could use what I learned here in future analysis while writing another paper. Also, I know now that obtaining texts digitally is not an easy task because of the copyright laws and that putting it into these programs is also quite difficult. While working with digital programs I also learned you must follow the steps and formats accordingly if not the program will not read the file and not go through with the commands. So, if you make a mistake it won’t work. I had fun with the Syllabi project because there were so many options to choose from. The only thing was that the internet was horrible during the two sessions we worked on the project. I tried using the Tool Jar program on the original syllabi, but it would not produce a valid result. I know that the file on a larger scheme had to be fixed and edited in order to be used for other types of projects like Cluster Analysis, Bootstrap and Oppose. I felt that the project was going to help us and let us focus on one of many different techniques we learned over the semester. I chose to do topic modelling. I separated the syllabus I wanted which was the new 240s into a folder. I set it apart on its own in a new folder and created a corpus for it. I also set up an output folder where the results of the tool jar program would separate it into topics. After I open the csv file that was TopicsInDocs and cleared the names of the files up I put it into topics and contributions. I saved the file and copied and pasted it onto rawio.Graph website. Then I organized by file name and finally got the results I wanted. I like that this course challenged the way I thought about text and taught to think beyond the regular close analysis. It also brought us up to date on how we can take text into our own hands and try to break the original concepts of analysis. It is only logical that as the world around us evolves our analysis does as well.
This course honestly was not at all what I was expecting. I started out thinking that it was just a senior seminar class that was going to culminate in a long thesis on some topic in English that I would probably never want to think about again. Instead, I was pleasantly surprised and greeted with an idea and approach that I had never encountered before. I was excited at the prospect of doing more hands-on kind of work, learning the ins and outs of a new system to help look at and analyze literature in more ways than just the “close read” method. After the first class, I even downloaded Rstudio to my laptop in my excitement and anticipation for what was to come.
I feel as though I have learned quite a few interesting tidbits about literature analysis that, although will probably not become a major part of my everyday vernacular, I will try to bring into use when I am teaching. I find the analysis of texts on the level of words to be amazing in terms of what we can learn and how we can compare texts. I was actually able to reference the work we did in this class the other day when I was student teaching and doing a lesson on diction. I, unfortunately, couldn’t show my students how exactly the program worked, but I felt accomplished to be able to describe to them how important diction is and how it can be analyzed and used to figure out whether an anonymous text was written by a specific author, or whether a text that is attributed to a specific author is actually written by that person. I hope to be able to maybe incorporate Rstudio into a lesson sometime in the future, where I can show students just how important diction is to an author’s writing. I think I would also be able to use things like Voyant and the topic modeling tool to help my students understand how different words, themes, and texts are connected and related to one another. Considering how integral technology is to younger students, introducing something like digital humanities might help to engage them more than just reading the text itself.
Overall, I really enjoyed this class. I would have liked there to be a bit more hands-on in-class work because I personally felt most accomplished and understood the concepts the most when I was able to create visualizations that I can look at an interpret myself. I hope to be able to use what I have learned in this class in my future teaching, even if it’s just to revisit what we have learned and maybe get some other students interested in analyzing literature without having to actually read a book.