( continuation from Part 1)
Text Analysis is not a super easy thing. In fact, this was probably the concept I was the worst at in all my English classes.
Text analysis allows up to dig deeper into a novel or longer text. A few examples of how text analysis is used are seeing the word choice that’s used or whether dialogue is present. There are lots of others, but I’d like to focus on those for this assignment.
This was a book I remember very well from one of my high school English classes. George and Lennie are migrant ranch workers who are stuck together in search of work during the Great Depression. I chose to use the video bubbles representation which shows (in a very fast pace) what words are used and how often. Voyant tools picked up on 6,113 words. What I learned from the visual representation is that there is a lot of dialogue. Of those 6,113 words, “said” was the second most-used word in the novel, indicating the characters were speaking a lot. Another thing I noticed was the use of southern slang, such as “ain’t”, “em”, and “jus”. This book does take place in a rural parts of California, which explains these slang terms.
For the novel I don’t know, I went to Project Gutenberg to select a popular novel to see if it’s visual representation is helpful. I chose Dracula, one I had heard of, but know nothing about. I chose the bubble visualization for this one (mainly because I find it cool and interesting). What I saw wasn’t exactly what I expected. Usually when you hear “Dracula”, you think vampires. Neither “Dracula” nor “vampire” were one of the most used words, if really used much at all. The most used word was “said” used 570 times out of 16,887 words, again, showing lots of dialogue. It shows me that two of the characters names are Mina and Jonathan, but doesn’t tell me much about the relationship the two have (or don’t have).
This quote, written by someone in the UCLA Digital Humanities discipline, sums up text analysis in the digital world quite well…
“While text analysis is considered qualitative research, the algorithms that are run by the tools are using quantitative methods as well as search/match procedures to identify the elements and features in any text.“