Introduction to Text Analytics with R – Part 8: SVD with R
This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques:
– Tokenization, stemming, and n-grams
– The bag-of-words and vector space models
– Feature engineering for textual data (e.g. cosine similarity between documents)
– Feature extraction using singular value decomposition (SVD)
– Training classification models using textual data
– Evaluating accuracy of the trained classification models
Part 8 of this video series includes specific coverage of:
– Use of the irlba package to perform truncated SVD.
– How to project a TF-IDF document vector into the SVD semantic space (i.e., LSA).
– Comparison of model performance between a single decision tree and the mighty random forest.
– Exploration of random forest tuning using the caret package.
The data and R code used in this series is available via the public