Text mining Praxis

We did the assignment recently for another class and I just to import it here since it concerns the text mining in particular. It was a group project and the text we chose was the time classic Dracula right around the Halloween. It was an annotating assignment but the text mining was important part of it. Our group took this text and we ran through the Voyant text mining program to find out which words were most used in this novel. The usual words such as vampire, time, etc were present but some were very interesting to say the least such as room or poor (which in Victorian times were ever present theme).

Text mining and text analysis are good tools since they save your time. I just imagine a time for Digital Humanists to comb through the novel hand by hand to count each words and tally them to get the whole picture. These types of assignment and exercises would not be even possible say couple of decades ago. These programs provide us a new tools to analyze the novel in more nuanced ways and find which rhetoric was employed in the novel. I want to bring in the works of Ian Bogost’s term of procedural rhetoric in analyzing the novels since we can infer the laws of that world through analyzing the words that are used in this Victorian novel.

There are a lot of words concerning communication in this novel and if we break down the novel using the definition of procedural rhetoric we can infer some things. Things such as communication pre 20th century was based on good listening, availability of candlelight and the prominent usage of letters for example since it is written in the late 19th century. A good handwriting was paramount and we start to understand the “laws” of that setting in more nuanced ways and the text mining give us the tools to explore them.

Deconstructing the text or any other media are important in understanding the building blocks of these works. These tools are convenient and powerful at the same time. They save time, and present an appealing platform to be digested by people who may not be tech savvy.