Author-Specific Linguistic Patterns Unveiled: A Deep Learning Study on Word Class Distributions

TL;DR

This paper explores author-specific linguistic patterns using deep learning to analyze word class distributions, demonstrating that bigram features significantly enhance author classification accuracy and reveal stylistic clusters.

Contribution

It introduces a deep learning approach to identify author-specific POS and bigram patterns, advancing computational stylistics and author profiling methods.

Findings

Bigram-based models outperform unigram models in author classification.

Deep neural networks effectively capture stylistic nuances through linguistic features.

Authors' works form meaningful clusters in feature space, reflecting stylistic similarities.

Abstract

Deep learning methods have been increasingly applied to computational linguistics to uncover patterns in text data. This study investigates author-specific word class distributions using part-of-speech (POS) tagging and bigram analysis. By leveraging deep neural networks, we classify literary authors based on POS tag vectors and bigram frequency matrices derived from their works. We employ fully connected and convolutional neural network architectures to explore the efficacy of unigram and bigram-based representations. Our results demonstrate that while unigram features achieve moderate classification accuracy, bigram-based models significantly improve performance, suggesting that sequential word class patterns are more distinctive of authorial style. Multi-dimensional scaling (MDS) visualizations reveal meaningful clustering of authors' works, supporting the hypothesis that stylistic nuances can be captured through computational methods. These findings highlight the potential of deep learning and linguistic feature analysis for author profiling and literary studies.