Detect a Writer’s Fingerprints Using Machine Learning

In this project, we will explore authorship attribution by analyzing the unique traits in an author’s written works. Our dataset comprises a collection of songs from well-known songwriters and includes song titles, lyrics, and author information. We will develop a model that will accurately attribute authorship to a given text. Such a model can have applications in various fields, such as plagiarism detection, literary analysis, and authorship attribution.

To get started, we will load the dataset and language model that will help us in processing the text. Then, we will preprocess the text to minimize noise and extract linguistic features that can help in identifying an author, for example, word length distribution, word frequency, and word co-occurrences. Next, we will learn to create a training corpus, and use it to attribute authorship to a text using Burrows's Delta.

By the end of this project, we will build a model that can attribute authorship with high accuracy. We will also explore how these techniques can be extended to analyze how an author’s style evolves over time.