Handling Text: Document Retrieval, Classification, Sentiment Analysis

This lecture covers the handling of text data, focusing on document retrieval, classification, and sentiment analysis. Topics include using TF-IDF matrices, nearest-neighbor methods, and the challenges of high model capacity. It also explores the use of matrix factorization for topic detection and the concept of latent semantic analysis. The lecture delves into the importance of regularization in machine learning models and introduces the concept of probabilistic topic modeling with Latent Dirichlet Allocation (LDA). Additionally, it discusses the transition from word vectors to contextualized word vectors and the role of models like BERT in natural language processing pipelines.