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Learning Stylometric Representations for Authorship Analysis
Authorship analysis (AA) is the study of unveiling the hidden properties of
authors from a body of exponentially exploding textual data. It extracts an
author's identity and sociolinguistic characteristics based on the reflected
writing styles in the text. It is an essential process for various areas, such
as cybercrime investigation, psycholinguistics, political socialization, etc.
However, most of the previous techniques critically depend on the manual
feature engineering process. Consequently, the choice of feature set has been
shown to be scenario- or dataset-dependent. In this paper, to mimic the human
sentence composition process using a neural network approach, we propose to
incorporate different categories of linguistic features into distributed
representation of words in order to learn simultaneously the writing style
representations based on unlabeled texts for authorship analysis. In
particular, the proposed models allow topical, lexical, syntactical, and
character-level feature vectors of each document to be extracted as
stylometrics. We evaluate the performance of our approach on the problems of
authorship characterization and authorship verification with the Twitter,
novel, and essay datasets. The experiments suggest that our proposed text
representation outperforms the bag-of-lexical-n-grams, Latent Dirichlet
Allocation, Latent Semantic Analysis, PVDM, PVDBOW, and word2vec
representations