Title of Poster or Presentation

Quantitative Methods of Evaluating Song Lyrics

Presenter Information

Timothy MitchellFollow

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Submission Type

Oral/Paper Presentation

Degree Type

Masters

Discipline

Sciences

Department

Math

Access Type

Open Access

Abstract or Description

Advances in text mining and natural language processing have made it viable to study text using methods normally reserved for numerical data. Here I present an analysis of song lyrics based on a data set of 200,000+ songs scraped from the web. I find that several summary statistics follow a smooth unimodal distribution, including total words, unique words, and percentage of words that are unique. These distributions differ as a function of genre, with large effect sizes observed. One of the biggest challenges in natural language processing is the development of tools to measure and score literary devices. I propose a novel framework to measure consonance scores and present an original unsupervised algorithm that can detect consonance in text data. These provide a statistical basis for comparing frequencies of literary devices across songs, genres, and artists.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

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Jun 6th, 10:00 AM Jun 6th, 11:00 AM

Quantitative Methods of Evaluating Song Lyrics

Advances in text mining and natural language processing have made it viable to study text using methods normally reserved for numerical data. Here I present an analysis of song lyrics based on a data set of 200,000+ songs scraped from the web. I find that several summary statistics follow a smooth unimodal distribution, including total words, unique words, and percentage of words that are unique. These distributions differ as a function of genre, with large effect sizes observed. One of the biggest challenges in natural language processing is the development of tools to measure and score literary devices. I propose a novel framework to measure consonance scores and present an original unsupervised algorithm that can detect consonance in text data. These provide a statistical basis for comparing frequencies of literary devices across songs, genres, and artists.