Hey all!

It’s been an awesome semester with all of you!

If you would like to learn about Leibniz, Fractals, and Theology then click here!

Thanks!

Hey y’all… Violent crime was a pretty interesting topic.

Here is a link to my page. http://scalar.usc.edu/works/violent-crimes/index

Adios from one of the two s

I have learned way more about ceramic surfaces than i can handle in the past 2 weeks.

heres a quick synopsis

have a great summer y’all

http://scalar.usc.edu/works/scalar-final/index

Happy tuesday

Here stands my last assignment
A Scalar project of generous
Proportions.  Though I never learned
How to wrap my text around a photo,
The experiment ended in happiness.
If hovering over my note
Had only provided a pop-up,
Without the necessary clicks
It would have been as good
As Jason’s GIFs!
Alas, my photos are all made
Of old people and tired writing
Instruments. Here’s the hook to my book:
http://scalar.usc.edu/works/historical-treatment-of-jews—including-shakespeare/index

My final paper/project related to the Somali Refugee Crisis is officially public on Scalar! After reading you will hopefully be informed about the problem, current policy and what being a Somali refugee in the United States really means. To check it out follow the link below.

http://scalar.usc.edu/works/us-refugee-relations/index

Note: this is my last assignment, final, paper, whatever of college ever! 🙁

Thanks for a fun class Professor Evans and the rest of #engl294!

I plan on doing my scholar project on the topic of Violent crimes. I plan to dissect different factor that go into to people committing “Violent Crimes”. One thing that will be interesting for the scalar project will be the use of multimedia for my topic. Hopefully it doesn’t get to gory.

Initially I was unsure of what Topic Modeling would look like, before assignment 7 was officially started I attempted to create a Topic Model on the firefox version of Zotero, which didn’t work due to one of the many technical difficulties that were experienced during this assignment. In order to Utilize the topic modeling program, Paper Machines, it was determined we also needed the standalone version of Zotero rather than the Firefox edition, we also needed a large amount of material to pull samples from in order to properly create the model. Other members of the class and myself collected a total of 97 novels for the program to sample from. Topic Modeling involves using a computer to take a plethora of texts, randomizing their word order and grouping and then taking samples of the raw word data and comparing it to total word count of the whole sample, as well as determining what words are grouped closely to the samples which allows the computer to begin sorting the words into categories they are associated with. The books we used were all written in the same time period in order to prevent slang from different time periods being registered as a topic. I encountered Technical problems that prevented me from producing my own topic model with Zotero so the categories I am observing the results of the 50 topic .mallet file that Professor Evans sent. Many of the categories generated contain proper names that give away what books they reference, such as line 4 “holmes man mr sir watson house judge elizabeth cried” obviously a theme related to the books “Sherlock holmes” by Arthur Conan Doyle which revolve around a detective, Sherlock Holmes, and his assistant Watson, elizabeth is watson’s love interest and as a detective, crimes often end with some involvement of a judge explaining the presence of the word. Other lines such as line 44 “good make give years leave till continued half answer” are very vague and offer little information to what the theme or books sampled could be. overall using Zotero as a Topic Modeling software was easy enough to understand and operate that I believe it could be adopted widespread as a tool for humanists who are uneasy with the mysteries of coding, however I believe that the technical issues involved with the program currently are limiting its potential.

When the phrase topic modeling was first thrown around in class, I had no clue to what the meaning was. To complete our assignment for topic modeling we started off by using Zotero. As a group we collected books for the 1800’s, which would be the basis for our final project. While finding authors and books was not hard, the sheer magnitude of books needed was quite a pain, but luckily we had many people, which sped up the process.

After finding our books things got quite difficult. For myself I had many problems regarding the software on my laptop. To start I was not able to generate a word cloud from the topics due to the fact that my java was not working correctly. After re-downloading java, the problem was still the same and had to turn to somebody with much higher computer skills. Without this help I would have been completely lost and would have given up, thus making it questionable and difficult for those who are not tech savvy. From the java problem next came a problem with Zotero standalone. Once again I would have been lost if it were not for someone that was much more computer savvy than myself. After deleting my current Zotero standalone and re-downloading it, things began to run more smoothly.

From this point the actual topic modeling began. To start we used paper machines, which produced a 3-word topic. With only 3 words, it’s difficult to understand and analyze the topic of a book, thus we choose to use another topic modeling platform called Mallet. Mallet worked similarly to paper machines, although it produced result that had up to 10 topics. With 10 topics it allowed a more specific result such as “ mrs crawley, lufton, mr robarts, bishop, lady grantly, lord”. With this many results it is able to tell the book and the topic and theme from this book. If I had a necessary project in which it called me to use topic modeling I would use Mallet because it is more precise. Overall topic modeling is still difficult for me to fully understand, but I have a better understanding of it now.

As a class we gathered almost 100 books and imported them into a group Zotero folder. By doing this we provided ourselves with a wide array of novels produced in the 1800s to utilize for our topic modeling assignment. Topic modeling is defined as a statistical model for discovering abstract topics with in a set of documents. The machine itself identifies patterns and that groups words into different topics. This kind of tool would definitely be handy for someone who analyzes a lot of bodies of work or who read a lot of material. The tool allows someone to, without reading any of the texts, have a pretty good idea about what they are about and what kind of subjects are being dealt with.
However, in our collection, because there are so many books with so many different names and characters I felt that the topic modeling basically just organized topics based on the characters and therefore our topics were the books themselves. Which shows that the tool is useful but not necessarily tells us what the books are about. As opposed to the Decretis example in which the main topics were more evident.
In our sampling produced on Paper Machine we all ended up with only 3 word topics, which didn’t provide very much for analysis. My most evident topic was “sea, whale, men” which was obviously from Moby Dick, but all my others were relatively vague. However, from the 10-word topics produced on Mallet we were able to get a much better variety of topics. Line 18, which contains, “life, love, heart, death, father, nature, human, earth, mother” is more broad and produced a topic more related to family, the world and love. Then there are topic lines like, line 8, which has “pierre, thou, mother, madame, lucy, thee, mrs, Isabel, Robert” this line is much more reflective of likely one text. However, because I am not familiar with these books, none of them mean that much to me. If I was working on a project more catered to text I am familiar with however I think that this tool could be a lot more useful. Perhaps with more familiarity and time to perfect our use of Paper Machines the tool would be more useful but because I was only able to produce lines of 3 I feel that it was relatively unsubstantial.

To begin, the entire process from installation to execution was quite difficult. Besides the obvious difficulties regarding software, there was also the need for a large collection of texts. Due to collaboration, gathering approximately one hundred texts was not too difficult. However, working individually, it would be very hard to accumulate on enough texts to effectively use Paper Machines.

There was also some difficulty concerning the speed of the process. I went into paper machines preferences in Zotero to increase the memory allocation in an attempt to reduce the run time. I also reduced the topics from fifty to twenty because I felt that would give more meaningful results.

However, once we made it through the process the paper machines results were less informative than I expected. I felt the three words topics were not enough to accurately portray the authorship of the topics. As we discussed, paper machines is an attempt at a user-friendly software with similar functionality to mallet. However, the mallet output we looked at in class returned topics of eight to ten words, which made it a lot easier to discern the specific text and/or author related to the topic. With three words the topic results in paper machines were often too generic. For example, the topic “great, love, family” are very general themes that appear in many novels. Another very generic topic result I got was “people, character, earlier” which are not strong topics or themes. On the contrary, the topic “Catherine Heathcliff etc” from mallet was easy to identify as Wuthering Heights.

Overall, I think Mallet provided better topics and learning to use Mallet would have been comprably difficult to the entire installation and set up necessary for Paper Machines. I believe the idea behind Paper machines of a user-friendly software for topic modeling is a great idea, however, I feel it has been poorly executed by paper machines.