Author Identification for LiveJournal

1
Author Identification for LiveJournal

• Alyssa Liang

2
The problem

• LiveJournal a blogging website
• Given a document (an entry), identify the author
• Hierarchical classification
• first classify by gender
• then classify author based on gender

3
Features

• Unigrams Bigrams
• Average sentence and word length
• Number of words and distinct words
• Number of sentences in paragraph
• Number of UPPERCASE characters
• Number of words not in the dictionary
• Number of words with length lt 4
• Number of characters in italics, bold, and
  striked out

4
The 3 Classifiers

• Naïve Bayes generative model
• Assumes features in document are independent of
  each other
• Implemented multi-variate Bernoulli model
• Only represented if feature appeared in document,
  not number of times feature appears
• Decision Trees
• An internal nodes is a test of a feature, and
  each branch from the node represents the values
  it can take
• A leaf node represents a classification
• Build a smallish tree from the training data
  using minimum average entropy
• Maximum Entropy conditional model
• model all that is known and assume nothing is
  unknown
• Tries to find most uniform model that satisifies
  constraints, i.e. maximize the entropy

5
Results
Hierarchical

• Hierarchical classification has no benefits
• Need to improve gender classification could use
  different features

• Feature Reduction (on gender classification)
• took 512 most important features and reran maxent
  training then took 256 most important features,
  etc.
• Proved to be very stable
• Best features consisted mostly of bigrams (many
  of which contained punctuation).
• Also chose features where there was a large
  difference between male and female (number of
  distinct words, UPPERCASE letters, short words)