Identifying Maturity Rating Levels of Online Books

Abstract

With the huge amount of books available nowadays, it is a challenge to determine appropriate reading materials that are suitable for a reader, especially books that match the maturity levels of children and adolescents. Analyzing the age-appropriateness for books can be a time-consuming process, since it can take up to three hours for a human to read a book, and the relatively low cost of creating literary content can cause it to be even more difficult to discover age-suitable materials to read. In order to solve this problem, we propose a maturity-rating-level detection tool based on neural network models. The proposed model predicts a book’s content rating level within each of the seven categories: (i) crude humor/language; (ii) drug, alcohol, and tobacco use; (iii) kissing; (iv) profanity; (v) nudity; (vi) sex and intimacy; and (vii) violence and horror, given the text of the book. The empirical study demonstrates that mature content of online books can be accurately predicted by computers through the use of natural language processing and machine learning techniques. Experimental results also verify the merit of the proposed model that outperforms a number of baseline models and well-known, existing maturity ratings prediction tools.