LSA, VSM, & SVD
This video series includes specific coverage of latent semantic analysis (LSA), vector space model (VSM), & singular value decomposition (SVD)
The trade-offs of expanding the text analytics feature space with N-Grams.
What You'll Learn
> How bag-of-words representations map to the vector space model.
> Usage of the dot product between document vectors as a proxy for correlation.
> LSA as a means to address the curse of dimensionality in text analytics.
> How LSA is implemented using SVD.
> Mapping new data into the lower dimensional SVD space.
Text Analytics tutorial slides can be accessed here
Download R here
SMS Spam Collection Dataset used in this tutorial can be accessed here
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