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Improving Gender Classification of Blog Authors

Improving Gender Classification of Blog Authors. Arjun Mukherjee Bing LIu UIC. Introduction. Dataset 3100 blogs ( 1588 – men,1512 – women ) Related work

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Improving Gender Classification of Blog Authors

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  1. Improving Gender Classification of Blog Authors Arjun Mukherjee Bing LIu UIC

  2. Introduction • Dataset • 3100 blogs ( 1588 – men,1512 – women ) • Related work • Current systems use POS n-grams, word classes, personality types to capture stylistic behavior of authors’ writings for classifying gender. • However, these works use only one or a subset of the classes of features. None of them uses all features for classification learning.

  3. Feature Engineering and mining • F-Measure ( Not the accuracy!!! ) • F-Measure is used to measure contextuality and formality • F = 0.5 * [(freq.noun + freq.adj + freq.prep + freq.art) – (freq.pron + freq.verb + freq.adv + freq.int) + 100] • Stylistic features • Words such as lol, hmm and smileys determine style of person

  4. Feature….(contd) • Gender Preferential features • Females tend to post emotionally. Frequent use of intensive adverbs ( terribly, awfully….) whereas men tend to more provocative

  5. Features…( cont. ) • Factor analysis and word classes • Factor or word factor analysis refers to the process of finding groups of similar words that tend to occur in similar documents. • POS sequence pattern • A POS sequence pattern is a sequence of consecutive POS tags that satisfy some constraints • POS ngrams are good at capturing the heavy stylistic and syntactic information. Instead of using all such n-grams, we want to discover all those patterns that represent true regularities, and we also want to have flexible lengths

  6. Features…( contd.. ) • POS Sequence patterns • mining algorithm mines all such patterns that satisfy the user-specified minimum support (minsup) and minimum adherence (minadherence) thresholds or constraints.

  7. POS sequence algo.

  8. Feature Selection • System uses EFS( Ensemble feature selection ) algorithm for selecting the fearutes. • EFS is a hybrid of filter and wrapper techniques • Some of the criteria used for feature selection are information gain, Mutual information, Chi square test • Feature value assignment • The values are either boolean or term frequency

  9. Experiments and results • Classifiers • Naïve Bayes • SVM • SVM Regression

  10. Results

  11. Comparison results

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