Text analytics fundamentals cover:
– The importance of splitting data into training and test datasets
– Stratified sampling of imbalanced data using the caret package
– Representing text data for the purposes of machine learning
– Introduction to tokenization, stop words, and stemming
– The bag-of-words model for text analytics
– Text analytics considerations for data pre-processing
The data and R code used in this series is available here:
https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R
Table of Contents:
0:00 Overview
15:17 Representation
16:20 Tokenization
18:32 Document frequency matrix
20:38 Hypothetical DFM
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