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Introduction to NLP and Topic Modeling

Open Data Science via YouTube

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Overview

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This introductory session teaches the basics of analyzing text data with NLP, including preprocessing, vectorization, word embeddings, and topic modeling. It explains LDA and briefly introduces contextualized topic models.

Syllabus

Intro
Terminology: NLU vs. NLP vs. ASR
Applications of NLP
NLP Basics: Pre-processing
NLP Tools - Regular Expression IV
NLP Tools - Spacy vs. NLTK
Stemming
Lemmatization
Stopwords
Part of Speech (POS) Tagging
Terminology-Corpus
TF Vectorization !
TF Vectorization II - sklearn
Word Embedding - Learning • The basic idea of learning neural network word embeddings
FastText-gensim
Latent Dirichlet Analysis (LDA)
Contextualized Topic Models

Taught by

Open Data Science

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