Learning Stylometric Representations for Authorship Analysis.

Area of Science:

• Computational Linguistics
• Natural Language Processing
• Machine Learning

Background:

• Authorship analysis (AA) identifies author characteristics from text but often relies on manual feature engineering.
• Manual feature selection is dataset- and scenario-dependent, limiting generalizability.
• Previous methods struggle with the nuances of writing style extraction.

Purpose of the Study:

• To propose a neural network approach for automated authorship analysis.
• To incorporate linguistic features into distributed word representations for style learning.
• To develop a method that learns writing style representations from unlabeled texts.

Main Methods:

• Proposed a neural network model to mimic human sentence composition.
• Integrated topical, lexical, syntactical, and character-level linguistic features.
• Extracted these features as stylometric vectors for documents.
• Evaluated on authorship characterization, identification, and verification tasks.

Main Results:

• The proposed text representation significantly outperformed traditional stylometrics.
• Achieved superior performance compared to n-grams, LDA, LSA, and word2vec models.
• Demonstrated effectiveness across diverse datasets including Twitter, blogs, reviews, novels, and essays.

Conclusions:

• The neural network approach effectively learns writing style representations from unlabeled text.
• This method overcomes limitations of manual feature engineering in authorship analysis.
• The proposed technique offers a robust and generalizable solution for various authorship analysis tasks.