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Literary Style Classification with Deep Linguistic Features

Explore how people in the same profession share similar literary styles and how automatic classification can be achieved. This study identifies unique linguistic features in the texts of an entertainer, politician, and IT guru on Twitter. Various classifiers and feature extraction methods are evaluated for their effectiveness in categorizing professionals based on their writing style. The study found that Naïve Bayes Classifier performs well without feature engineering, while SVM requires careful feature selection to prevent overfitting.

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Literary Style Classification with Deep Linguistic Features

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  1. Literary Style Classification with Deep Linguistic Features Hyung Jin Kim Minjong Chung Wonhong Lee

  2. Introduction Basic Idea People in the same professional area share similar literacy style. Is there any way to classify them automatically? Entertainer (Britney Spears) “I-I-I Wanna Go-o-o by the amazing” Politician (Barack Obama) “Instead of subsidizing yesterday’s energy, let’s invest in tomorrow’s.” IT Guru (Guy Kawasaki) “Exporting 20,439 photos to try to merge two computers. God help me.” 3 different professions on Twitter

  3. Approach We concentrated on extracting as many features as possible from sentence, and selecting the most efficient features from them. Classifiers Support Vector Machine (SVM) Naïve Bayes (NB)

  4. Features • Basic Features • Binary value for each word => Large dimension of feature space • Word Stemming & Removing Stopwords • Used TF-IDF Weighting • Syntactic Features • POS Tag • Using specific classes of POS tag (e.g. only nouns or only verbs, etc.)

  5. Features • Manual Features • Limitation of performance with using only automatically collected features • Manually selected features by human intelligence!

  6. Feature Selection • TF-IDF (Term Frequency – Inverse Document Frequency) • Usually used as a importance measure of a term in a document • Information Gain • Tried to measure how we can reduce the uncertainty of labels if we know a word (or feature) • Chi-square • Tried to measure dependency between the feature and the class

  7. Implementation NLP Libraries • Stanford POS Tagger • JWI WordNet Package developed by MIT for basic dictionary operations Caching System • Quite amount of time to process • Devised our own caching system to enhance the productivity

  8. Result Performance of Various Feature Extractors on SVM SVM with TF-IDF selection : 60% SVM with IG selection : 58% SVM with Chi-square : 52%

  9. Result Performance of Manual Feature Extractors on SVM SVM with manual selection : 62%

  10. Result Performance of Classifiers (Naïve Bayes vs. SVM) NV without feature selection : 84% Random guess (benchmark) : 33%

  11. Conclusion • Naïve Bayes Classifier works surprisingly well without any feature engineering due to appropriate independence assumption • For SVM, selecting proper features is critical to avoid overfitting • Using only noun features works better than general

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