-After reading the texts from Belvins and Posner, I thought Topic Modeling consisted of categorizing words off of relative meanings. After using the tool and returning to the texts, I now have a better understanding of what topic modeling is! To make it simple, topic modeling is the list of words that appear together. Last week, we used the Voyant tool which allowed us to count words and look at statistical data of our texts, the Topic Modeling Tool allows us to look at the words that appear together. In my test, I chose to use the Book “Frankenstein” by Mary Shelley. I chose this book because I felt as Shelley’s use of emotions would be placed in similar areas. These are my results:

As you can see, the data sets are pretty accurate with my hypothesis. The fifth line shows the feelings Frankenstein has towards people. In my second book, I chose “The Great Gatsby” by Scott Fitzgerald. This is where the TMT tool really showcased a “distant” reading.

If you look through these lines, you can see in line three the word “love” and “Daisy” are both here. That is because our main character is in love with Daisy.

-I put the sentence “Digital Humanities is useful because” and the response I got was:

Honestly, this result I got was concerning. Instead of offering me a definition or a well-structured answer, it gave me more of an opinion. There is no substance to this and not really an answer. I feel like if I re-generated it, I would get a different answer. For educational purposes, I’m going to generate it again! 🙂

Hmm, pretty funny of the first one was more of an opinion and this one is a definition. This answer is the exact answer I would expect an algorithm to give me. I am pleased with the second response!

-Overall, I think algorithms are able to gather a large set of data and break it down into categories or limitations that you can set. I think the average person does not have time or the capability of running through these large data sets so it is helpful but the accuracy and quality is not as good as a human.