As digital humanities include the analysis of text, it is interesting to see the methods of analysis on the text given to undergraduates.

To explain this is to first break down what text analysis is. As John Laudun has done in his work, he presents what his analysis is very well: “First, we want to know the overall length of the story, and we want to know some basic information like how many words, sentences, and paragraphs are used to tell the story, or make the argument in the case of essays. (This kind of information is useful for later mapping our the overall shape and structure of a text.) Second, we want to know how many of the words in the word count are actually unique, what those words are, and which words get used the most often and which the least. (This establishes the vocabulary used, points to any particular registers, and begins to reveal the interaction between words and meaning.) Third, using the word frequency distribution, the fancier term for counting individual words, we want to visualize the text both as a graph and as a word cloud. In doing so, we can begin to ‘see’ for ourselves which words matter and which words don’t matter. (This introduces the idea of function words in the form of a word stop list.) Fourth, we want to use our new-found insight into word usage to examine particular instances: we want to see words in context and we want to see what words mean within the context of a particular text. (This highlights the role of context and offers a companion to meaning to be found simply in the words used.)”

By breaking down texts, undergraduate students are able to better understand the text more conceptually towards its structure according to how it is written. Emphasis on words through their use can express the greater meaning not only of the word to the passage, but to the passage itself. Using programs like Wordle to make word clouds, students can visually see the usage of each word in a passage where its size represents how often it is used.

As Wordle is just one example of an activity that can be used as an analysis of text technique, Adeline Koh has many other tools that she uses in her Introducing Digital Humanities Work to Undergraduates: An Overview. Koh’s other techniques may seem very basic, they are very well taken to by introductory students.

Using programs such as Python can be a little more advanced for text analysis compared to Koh’s techniques and Wordle. As Laudun and Hemphill have modified scripts to use for their own analysis programs, it is not as simple for undergraduate students to follow without previous understanding of what Python is and how to properly use it.

Thankfully for the sake of Hemphill’s Research Methods in Digital Humanities undergrad students, when comparing texts as required for their course, Hemphill understand that the students will “fail a lot”. This is very beneficial as some are still confused on the process of using some of the programs that are a part of the course.

Sources: http://libbyh.com/2014/10/16/introducing-text-analytics-to-undergraduates/ http://johnlaudun.org/20130221-text-analytics-101/ http://www.digitalpedagogylab.com/hybridped/introducing-digital-humanities-work-undergraduates-overview/