Module 2: Chapter 4 Classic NLP Techniques

This video presents a hands-on walkthrough of Chapter 4 NLP concepts using the Toxic Comment Dataset. The notebook focuses on moving beyond basic keyword matching and introducing semantic analysis, topic modeling, dimensionality reduction, and text classification. The video covers how text is transformed into numerical representations using TF-IDF, then reduced into lower-dimensional semantic spaces using LSA/Truncated SVD. It also explores Latent Dirichlet Allocation (LDA) for topic modeling and shows how topic vectors can be used as features for downstream classification tasks. Topics demonstrated include: Toxic vs. non-toxic comment analysis TF-IDF document vectors Semantic centroids and toxicity scoring Linear Discriminant Analysis classification Training and test evaluation Confusion matrix interpretation Principal Component Analysis (PCA) Latent Semantic Analysis (LSA) Truncated SVD and topic vectors Topic-term matrices Latent Dirichlet Allocation (LDA) Semantic similarity and document retrieval Semantic search concepts Using topic vectors as machine-learning features The notebook also demonstrates an important practical lesson: real NLP workflows can be affected by missing datasets, unavailable external resources, library-version differences, and code errors, so troubleshooting is part of the development process.