LSA, VSM, & SVD – Introduction to Text Analytics with R Part 7
Part 7 of this video series includes specific coverage of LSA, VSM, & SVD:
– 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.
Kaggle Spam Data Set
The data and R code here
Introduction to Text Analytics with R
More Data Science Material:
[Video] Introduction to Natural Language Processing
[Blog] Liberating the Data Artist: Rethinking Creativity in Data Science