Topic Modeling Workshop for Beginners in Python

This beginner workshop demonstrates topic modeling in Python using NIPS papers, covering text preparation, exploratory analysis, tokenization, LDA model building, visualization, and coherence-score evaluation.

Syllabus

- Tutorial Starts
- Topic Modeling Intro
- Workshop Environment
- Content location at GitHub
- Dataset used in this workshop
- LDA Intro
- Topic Modeling Use Cases
- 6 Steps in this Workshop
- Step 1: Loading Data
- Step 2: Data Preparation
- Step 2.1: Removing Punctuation
- Step 2.2: Removing digits and word with digits
- Step 2.3: Lowercase all context
- Step 3: EDA
- Step 3.1: Word Cloud
- Step 3.2: Document Term Matrix
- Step 4: Data Modeling
- Step 4.1: Stop words removal
- Step 4.2: Creating Bigram and Trigram
- Step 4.3: Lemmatization
- Step 4.4: Tokenization
- Step 5: LDA Topic Modeling
- Step 6: Topic Modeling Performance and analysis
- Step 6.1: Topic visualization
- Step 6.2: Coherence Score
- Saving notebook to GitHub
- Recap