Uncovering Black Fantastic: Piloting A Word Feature Analysis and Machine Learning Approach for Genre Classification

Abstract

Given the size of digital library collections and the inconsistencies in their genre‐related bibliographic metadata, as digital libraries grow and their contents are opened for computational analysis, finding materials of interest becomes a major challenge. This challenge increases for sub‐genres and other categories of text data that are less distinct from the whole. This project pilots machine learning methods and word feature analysis for identifying Black Fantastic genre texts within the HathiTrust Digital Library. These texts are sometimes referred to as “Afrofuturism” but more commonly today described as “Black Fantastic,” in which African Diaspora artists and creators engage with the intersections of race and technology in their works with a primary focus on world‐building. Black Fantastic texts pose a challenge to genre classification, as they incorporate aspects of science fiction and fantasy with typical characteristics of African Diaspora‐produced literature. This paper presents and reports on results from a pilot predictive modeling process to computationally identify Black Fantastic texts using curated word feature sets for each class of data: general English‐language fiction, Black‐authored fiction, and Black Fantastic fiction.

Comparative Analysis of Deep Learning Architectures and Vision Transformers for Musical Key Estimation

“…In addition to the aforementioned papers, several other studies have made significant contributions to the field of music research. The authors of [39] employed machine learning and word feature analysis to identify texts belonging to the Black Fantastic genre within the HathiTrust Digital Library. This project presents a pilot predictive modeling process that computationally identifies these texts by leveraging curated word feature sets for each data class.…”