Originally published at: https://tutorials.datasciencedojo.com/text-analytics-r-vsm-lsa-svd/
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 Dataset can be found here
The data and R code used in this series is available here
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