LSA, VSM, & SVD
r_subheading-Course Description-r_end This video series includes specific coverage of latent semantic analysis (LSA), vector space model (VSM), & singular value decomposition (SVD). r_break r_break The trade-offs of expanding the text analytics feature space with r_link-N Grams- https://online.datasciencedojo.com/course/Text-Analytics-with-R/model-building-and-evaluation/n-grams-r_end. r_break r_break r_subheading-What You'll Learn-r_end • How bag-of-words representations map to the vector space model. r_break • Usage of the dot product between document vectors as a proxy for correlation. r_break • LSA as a means to address the curse of dimensionality in text analytics. r_break • How LSA is implemented using SVD. r_break • Mapping new data into the lower dimensional SVD space.
Text Analytics tutorial slides can be accessed r_link-here- https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R-r_end r_break r_break Download R r_link-here- https://cran.r-project.org/-r_end r_break r_break SMS Spam Collection Dataset used in this tutorial can be accessed r_link-here- https://www.kaggle.com/uciml/sms-spam-collection-dataset-r_end
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