Charleston Lee

M.S. Data Science

Investigating Criminal References in Rap Lyrics: A Data Science Approach

Rap music has long served as a platform for artists to express their realities, often exploring themes of crime and street life. This study employs data science methodologies to identify and analyze potential references to criminal activities within rap lyrics. Leveraging natural language processing techniques, this study analyzes a curated dataset of rap lyrics spanning the years 2000-2023, sourced from a diverse range of music labels, certifications, and artists, encompassing modern slang associated with criminal behavior while ensuring ethical data collection practices. Through sentiment analysis, topic modeling, and named entity recognition, the aim of this study is to quantify and contextualize the prevalence of criminal references in rap songs during this time period. Additionally, this study investigates the relationship between lyrical content and commercial success by examining the impact of identified themes on the performance of songs, measured through RIAA certifications from 2000-2023. Furthermore, based on these linguistic insights, a chatbot is developed that is equipped with a comprehensive understanding of contemporary slang terminologies related to crime. This chatbot enables interactive engagement and discourse on pertinent subjects within the rap genre, facilitating broader discussions about cultural representations in music and industry influences. This interdisciplinary approach not only advances data science methodologies but also provides valuable insights into the portrayal of societal realities within artistic expression and its reception in the music industry across different labels and time periods.