Comparing Writing Styles using Word Embedding and Dynamic Time Warping

This flow can be quantified and compared by analyzing the text using natural language processing techniques. This study uses word embedding models to generate time series for novels and then compare the resulting series using dynamic time warping to find similarities. Considering time series analysis rather than a pure statistical one can capture the flow of the works and thus the similarities generated will provide a metric for comparing the novels based on the progression.