期刊
INFORMATION
卷 11, 期 12, 页码 -出版社
MDPI
DOI: 10.3390/info11120589
关键词
authorship; age; text analysis; computer vision; social networks; FastText; VGG-Face; CRNN
资金
- Ministry of Digital Development, Communications and Mass Media of the Russian Federation
- Russian Venture Company (RVC JSC) [009/20]
This paper is devoted to solving the problem of determining the age of the author of the text based on models of deep neural networks. The article presents an analysis of methods for determining the age of the author of a text and approaches to determining the age of a user by a photo. This could be a solution to the problem of inaccurate data for training by filtering out incorrect user-specified age data. A detailed description of the author's technique based on deep neural network models and the interpretation of the results is also presented. The study found that the proposed technique achieved 82% accuracy in determining the age of the author from Russian-language text, which makes it competitive in comparison with approaches for other languages.
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