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Explore advanced text analytics techniques in this 38-minute video tutorial focusing on Vector Space Model (VSM), Latent Semantic Analysis (LSA), and Singular Value Decomposition (SVD). Examine the trade-offs of expanding the feature space with n-grams and understand how bag-of-words representations map to VSM. Learn to use dot product between document vectors as a correlation proxy. Dive into LSA as a solution to the curse of dimensionality in text analytics and discover its implementation using SVD. Master the process of mapping new data into the lower dimensional SVD space. Access accompanying data and R code for hands-on practice, enhancing your text analytics skills with R programming.
