The Word2vec Graph Model for Author Attribution and Genre Detection in Literary Analysis

TL;DR

This paper introduces a novel Word2vec graph model for literary analysis tasks like author attribution and genre detection, capturing context and style more effectively than traditional features.

Contribution

The paper presents a new Word2vec graph-based document modeling approach that improves classification accuracy in literary analysis tasks across different datasets.

Findings

Outperforms traditional feature-based methods in author attribution

Effective in genre detection across diverse literary datasets

Code and data are publicly available for reproducibility

Abstract

Analyzing the writing styles of authors and articles is a key to supporting various literary analyses such as author attribution and genre detection. Over the years, rich sets of features that include stylometry, bag-of-words, n-grams have been widely used to perform such analysis. However, the effectiveness of these features largely depends on the linguistic aspects of a particular language and datasets specific characteristics. Consequently, techniques based on these feature sets cannot give desired results across domains. In this paper, we propose a novel Word2vec graph based modeling of a document that can rightly capture both context and style of the document. By using these Word2vec graph based features, we perform classification to perform author attribution and genre detection tasks. Our detailed experimental study with a comprehensive set of literary writings shows the effectiveness of this method over traditional feature based approaches. Our code and data are publicly available at https://cutt.ly/svLjSgk