VSM, LSA, & SVD | Introduction to Text Analytics with R | Part 7

An overview of the Vector Space Model (VSM), Latent Semantic Analysis (LSA), and Singular Value Decomposition (SVD). This talk will cover:
– The trade-offs of expanding the text analytics feature space with n-grams.
– How bag-of-words representations map to the vector space model (VSM).
– Usage of the dot product between document vectors as a proxy for correlation.
– Latent semantic analysis (LSA) as a means to address the curse of dimensionality in text analytics.
– How LSA is implemented using singular value decomposition (SVD).
– Mapping new data into the lower dimensional SVD space.

The data and R code used in this series is available here:
https://code.datasciencedojo.com/data...

Table of Contents:
0:00 Introduction
11:37 Vector space model
12:53 Visualizing
15:06 Dot product
19:13 Matrix multiplication
25:11 LSA

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