Day 1

IST 736

1.1 Readings

1.2 Student intro

• Word Cloud Generator

1.3 Text Representation/Vectorization

1.4 Exploratory Text Mining

• Corpus Statistics
• Document Clustering
• Topic Modeling

1.5 Predictive Text Mining

• Text categorization
  • Sentiment classification
  • News topic classification
  • Genre classification
  • USING: Naive Bayes and SVM algorithms
• Regression problems

1.6 Difference between Text Mining and NLP

• NLP
  • Deep linguistic analysis
  • (May take a long time to analyze large collections)
• Text Mining
  • Shallow analysis (e.g. N-grams) for quick analysis of large collections
  • (Sometimes use deep NLP features like PoS tags or dependencies for feature engineering)

1.7 Class Policies

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UNSCHOOLING:

Khan Academy

• Computer Science – Algorithms: Binary Search