5 Text Analytics
5.4 Predictive Coding: Bringing Text Analytics to the Courtroom (Recitation)
Video 1: The Story of Enron
The slides from all videos in this Recitation can be downloaded here: Bringing Text Analytics to the Courtroom (PDF).
Video 2: The Data
In this recitation, we’ll be using the dataset energy_bids (CSV - 2.0MB). Please download and save this dataset to your computer so that you can follow along. This data comes from the 2010 TREC Legal Track.
An R script file with all of the commands we will be using in this recitation can be downloaded here: Resource Unit5_Recitation (R).
Video 3: Pre-Processing
Important Note: In the following video, we ask you to use the “tm” package to perform the pre-processing steps. Due to function changes that occurred after this video was recorded, you will need to run the following command immediately after converting all of the words to lowercase letters (it converts all documents in the corpus to the PlainTextDocument type):
corpus = tm_map(corpus, PlainTextDocument)
Then you can continue with the R commands as they are in the video.
If the code length(stopwords(“english”)) does not return 174 for you, then please run the line of code in stopwords (TXT) file, which will store the standard stop words in a variable called sw. When removing stop words, use tm_map(corpus, removeWords, sw) instead of tm_map(corpus, removeWords, stopwords(“english”)).
Video 4: Bag of Words
Video 5: Building Models
Video 6: Evaluating the Model
Video 7: The ROC Curve
Video 8: Predictive Coding Today
Course Info
Instructor
Departments
As Taught In
Spring
2017
Level
Learning Resource Types
theaters
Lecture Videos
notes
Lecture Notes
assignment_turned_in
Problem Sets with Solutions
