How does text become data?
summary
Shows how raw text becomes usable vectors and facts using concrete tools and numbers: tokenization with nltk, stemming via WordNet, bag‑of‑words + tf-idf vectors, naive Bayes on 2,000 movie reviews hitting 80% accuracy, LSI/GENSIM compressing a 39,000‑word dictionary into 50 topics, and ConceptNet’s background knowledge with ~17 million edges. Demonstrates code (Python, scikit‑learn, gensim, requests) and a working product (Luminoso) that finds similar documents, extracts collocations, and augments corpus‑trained meaning with general commonsense relations.
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chapters (7)
- 0:00Introduction
- 3:36Code examples
- 7:58Data visualization
- 12:47Classification
- 18:29Text
- 25:01Word Meaning
- 29:04ConceptNet
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