Age-Suitability Prediction for Literature Using a Recurrent Neural Network Model
Eric Brewer, Yiu‐Kai Ng · 2019
Digital media holds a strong presence in society today. Providers of digital media may choose to have their content undergo an age-suitability analysis before being published. These analyses provide ratings that denote to which age group(s) a particular media item is appropriate. Content rating systems exist in many countries for television, music, video games, and mobile applications. These systems allow consumers to quickly determine whether or not a given media item is suitable to their age or preference. Literature, on the other hand, remains devoid of a comparable rating system. If a new, human-driven rating system for literature were to be implemented, it would be impeded by the fact that literary content is produced far more rapidly than are other forms of digital media; human working within such a system simply would not be able to read and analyze literature at its current rate of production. Thus, to provide fast, automated age-suitability ratings to works of literature (i.e., books), we propose a computer-driven rating system which predicts a book's content rating within each of eight categories: (a) crude humor/language; (b) drug, alcohol, and tobacco use; (c) kissing; (d) profanity; (e) nudity; (f) sex and intimacy; (g) violence and horror; and (h) gay/lesbian characters given the text of that book. Our computer-driven system circumvents the major hindrance to any theoretical human-driven rating system previously mentioned, i.e., infeasibility in time spent.