This project was created by Natalie Kretschmer, Theodore Manning, Faihaa Khan, Nuraly Soltonbekov, and Brianna Caszatt for the ENGL 720 course Textual Studies in the Digital Age: Doing Things with Novels, Fall 2022 semester with Professor Jeff Allred.
The text of Dracula came from this edition in Project Gutenberg. Each post is numbered sequentially, in the order in which it appears in the text. There is an H2 at the beginning of each post making it clear whose point of view it was from (in some cases this did mean adding text where it did not exist in the original text, but we used the same wording that Bram Stoker used, e.g., “Jonathan Harker’s Journal (continued)”). Some journal and diary posts started with a date and then had entries marked as “Later” but of the same date; these all appear as one post. The exception is when an entry of the same date was was broken up by a new chapter, in which case the chapter breaks were preserved for this project.
Each post is categorized according to the chapter in which it appeared. Each post also has multiple tags. There are month and date tags, corresponding to the month and the date associated with each section of text. The posts are also tagged according to what type of text they are: journal and diary entries, letters, telegrams, memoranda and notes, ship’s log, and newspaper clippings. There are also tags corresponding to whose diary or journal the text came from: Jonathan’s journal, Mina’s journal, Dr. Seward’s diary, and Lucy’s diary And as there is much exchange of correspondence throughout the novel, so there are tags for each of the primary characters’ correspondence (including that which they sent and also received): Jonathan’s, Mina’s, Lucy’s, Dr. Seward’s, Van Helsing’s, Arthur’s, Quincy’s, and Dracula’s. There are also several instances in the story where correspondence is not delivered or received by the intended recipient, so there’s also a category for this: unopened or undelivered.
Google Ngram Viewer Results
Here are larger versions of the Google Ngram Viewer results shared in the annotations in post No. 3.
Voyant Tools Word Cloud
Here is the word cloud I created in with Voyant Tools and shared in an annotation in post No. 1.
Python and the Natural Language Toolkit
In a Jupyter notebook using Python 3, I imported the text of Dracula from Project Gutenberg, and tokenized it into a list of words and punctuation. I then removed all of the punctuation and converted the list into NLTK so I could use the similar function and test out a multiple words from the novel. Here are screenshots for some of the more interesting results.