Using MALLET I did a topic model on Looking Backward, 1887-2000 by Edward Bellamy, here are the corresponding topic-keys. There were 77,351 words and a total of 28 chapters of content after removing the preface and Gutenberg notes.
My purpose here is to briefly assess if using the Machine Learning for Language Toolkit (MALLET) for the exploration of literary corpora will correctly formulate the thematic intentions present in a particular book: Looking Backward, 2000-1887 by Edward Bellamy.
Since I will not be providing much of a book review here, I thought it best to supply some source material for those who are unfamiliar with the text.
So it begins.
Over the past 21-days, I have been collecting all of my keystrokes with Mac OSX Keylogger. As a result, I have come to realize my dependence on the delete key, habit of rephrasing and adjusting my word use, and likely over complicating my thought process.
It’s been about two weeks since I have last posted. As such, I am posting my weekly usage from January 18th to the 25th while I prepare my next more in-depth post. I have since made this switch from a Windows machine to a Mac, and I am currently running a different keylogger which can be found on GitHub. I will elaborate on this within the next post- hopefully no later than next week.
This graph includes all keystrokes, including non-characters and numbers. The graph was made in excel and edited with illustrator for those curious.
I have been tracking all of my keystrokes over the past week via the Xenotix Python Keylogger, and I found the results more interesting than I assumed they would be. Even though I only have seven days worth of data collected, it appears that it will be promising in long-term collection and analyzation.