确定在线图书的成熟度等级

Eric Brewer, Yiu-Kai Ng
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引用次数: 1

摘要

现在有大量的书籍可供选择,确定适合读者的合适的阅读材料是一项挑战,特别是适合儿童和青少年成熟水平的书籍。分析书籍的年龄适宜性可能是一个耗时的过程,因为一个人阅读一本书可能需要长达三个小时的时间,而创作文学内容的成本相对较低,这可能会导致发现适合年龄的阅读材料变得更加困难。为了解决这一问题,我们提出了一种基于神经网络模型的成熟度等级检测工具。所提出的模型在七个类别中预测一本书的内容评级水平:(i)粗俗幽默/语言;㈡吸毒、酗酒和吸烟;(3)接吻;(四)亵渎;(v)裸露;(vi)性和亲密关系;(七)暴力和恐怖,鉴于这本书的文本。实证研究表明,通过使用自然语言处理和机器学习技术,计算机可以准确地预测在线图书的成熟内容。实验结果还验证了该模型的优点,该模型优于许多基线模型和已知的现有成熟度评级预测工具。
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Identifying Maturity Rating Levels of Online Books
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.
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