Why You Should Do Text Analysis in Python (Even if You Don't Want to)

on YouTube ↗49m

" summary

Exports of group chats from WhatsApp exports get parsed year by year to count who starts conversations who uses the most emojis and to run topic models that surface shifts (World Cup, breakups, travel). Public work includes attributing Shakespeare passages via stylistic networks and separating Trump tweets by device and sentiment to spot different authors. The stack uses concrete tools: spaCy pipelines for POS and dependency queries, word2vec analogy tests and recurrent nets for generation — code and tutorials actually run and reproduce results.

AI-written summary · about

" chapters (11)

1. 0:00Text analysis with Python
2. 2:53Personal stories
3. 7:03Research and business uses
4. 9:54Finding text data
5. 12:43Pre-processing importance
6. 15:53Machine learning techniques
7. 17:59Word embeddings
8. 21:51Computational linguistics
9. 25:44Neural text generation
10. 29:55Resources and tutorials
11. 32:28Q&A

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