Vocabulary Analysis of Job Descriptions
summary
Processes a 4,300‑record corpus of US job descriptions using pandas and NLTK to tokenize, sentence‑detect, stem/lemmatize, filter stopwords and build n‑grams; implements an inverted index and an average TF.IDF calculation, producing word clouds and showing vocabulary jumps (token → stems → sentence n‑grams: ~16,000 stems, ~1600% and ~61% vocabulary increases). Models use scikit‑learn decision trees to predict education/experience/hourly flags with modest gains, reveal overfitting and SEO artifacts, and note pickled object compatibility issues on Windows.
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chapters (11)
- 0:00Vocabulary analysis intro
- 8:10Text vs structured data
- 13:58Meaning in text data
- 18:01Tokenization challenges
- 26:28TF-IDF for text analysis
- 34:33TF-IDF limitations
- 47:36Stemming vs lemmatization
- 1:06:10Removing stop words
- 1:26:15N-grams for context
- 1:47:14Modeling with TF-IDF
- 1:52:36Q&A
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