rdawood/MALI

Machine Assisted Literary Interpretation M.A.L.I ======= Machine Assisted Literary Interpretation By Raza Dawood A program that tokenizes texts and visualizes the output. Built using Python 2.7 and D3. ============================================================================== Folders and Files: MALI src --> __init__.py Initializes the programs directory to allow imports. It is intentionally blank and should not be edited. --> stopwords.txt A list of stop words compiled for use in the program. Using it is entirely optional and the user can replace the file with his/her own, so long as it is named 'stopwords.txt' and is placed in this folder. --> tokenizer.py Functions used to read a text, tokenize it, and create a frequency distribution of word use. --> visualizer.py Contains numerous helper functions for compiling the javascript necessary to render the program's graphical outputs. It reformats tokenized data from list and tuple structures to a string resembling JSON. Ultimately, it writes and calls the container.html file that contains the visualization. The program runs in a loop to allow the user to render multiple visualizations. --> d3.min.js Library for D3 (data driven documents). This is an open source javascript visualization library that MALI uses to render two visualization scripts that are written into visualization.py. --> [container.html] This file only appears after the program is run and a visualization is rendered. If the graph is not scaling properly (such as if lines are written on top of each other), maximize your screen and refresh the page (usually the F5 key). The graphs are designed for full-screen use. To save a certain graph for use later, copy the container.html file to a different directory. It is a self-contained script that has its data hard coded into it. Texts Author's Last Name --> .txt files with author's work. The program comes with sample texts from six separate authors. To add your own text, create a new folder under 'Texts', name it, and place the fi …