Abstract by Eric Brewer

Personal Infomation

Presenter's Name

Eric Brewer

Degree Level


Abstract Infomation


Computer Science

Faculty Advisor

Dennis Ng


Age-Suitability Prediction for Literature Using Deep Neural Networks


Digital media holds a strong presence in society today. Content rating authorities issue content ratings that denote to which age groups media items such as television, music, video games, and mobile applications are appropriate. Literature, however, remains devoid of a comparable content rating authority. If a new, human-driven rating authority for literature were to be implemented, it would be impeded by the fact that literary content is published far more rapidly than are other forms of digital media. Thus, to provide fast, automated content ratings to items of literature (i.e., books), we propose a computer-driven rating system which predicts a book's content rating within each of seven categories: 1) crude humor/language; 2) drug, alcohol, and tobacco use; 3) kissing; 4) profanity; 5) nudity; 6) sex and intimacy; and 7) violence and horror given the text of that book. Our work has demonstrated that mature content of literature can be accurately predicted through the use of natural language processing and machine learning techniques.