Jordan Carmer Portfolio Assignment

Textual Analysis Project

Textual analysis involves using a computer to analyze large collections of texts faster than what reading them by traditional means allow. By no stretch of the imagination does this replace actually reading the works, which allows for a more traditional analysis and an opportunity to employ critical thinking skills. But textual analysis does give teachers and students alike the chance to look at a text in ways that they might not have otherwise had the chance to do.

For this project, I used two corpus. Each is a collection 18th century British literary works. But one corpus (comprising 237 works) was written by women. The other (coming in at 214) was by men. When allowing Voyant to analyze either, it gave back a list of the most common words in each. As mentioned earlier, this is the "noise" of the initial analysis. But from these lists, I noticed that the word "woman" was withing the female authors' top fifty-five words, but not the male authors. This should seem somewhat natural, given that each sex is more likely to probably write about themselves. But it raised the question of how each sex viewed itself, and each other. By helping students to notice these patterns, we can show them how we can further manipulate raw data to see more detailed data hidden within.

Using these types of tools can help teachers get students to look at things in a way that might not have before. Most students can read a book. There are many who can even read and evaluate it really well. But no student has the time to read over 400 texts and critically consider all of them. And even less time to break them down by words. Tools like Voyant allow a different approach that wasn't available before. 

Below is a branch chart of words commonly associated with the word 'man' when written by male authors, followed by one representing when they wrote the word 'woman.' 



One might be quick to notice how often the word 'man' was followed or preceded by adjectives like 'biggest' and 'greatest.' They might also notice how often the word 'woman' was followed adjectives like 'young', 'obstinate', and 'bewildered' when written by the men. This can open a discussion for students around gender biases during the time these books were written. It presents information most students are going to tune in for, as some of the associated words are engaging in this context. Once the teacher has students engaged, it may be easier to keep them involved as the lesson progresses. They can also do what I've done here, which is create these same trees with the female corpus. In doing so, teachers can offer students a more complete picture with which to drive home their lesson. 



Note that aside from teaching students how to obtain this detailed data, it's important to teach them how to read and interpret it. For many students (and teachers), viewing data this way may be a new concept. For them to fully understand and appreciate it requires making sure they know they're looking at.