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Stylometry detection using deep learning

Publisher : Advances in Intelligent Systems and Computing, Springer Verlag

Source : Advances in Intelligent Systems and Computing, Springer Verlag, Volume 556, p.749-757 (2017)

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ISBN : 9789811038730

Keywords : Age predictions, Artificial intelligence, Data mining, Deep learning, Learning algorithms, Readability metrics, Relevant features, Stylometric features, Stylometry, Vocabulary richness

Campus : Kochi

School : School of Medicine

Department : Physical Medicine & Rehabilitation

Year : 2017

Abstract : Author profiling is one of the active researches in the field of data mining. Rather than only concentrated on the syntactic as well as stylometric features, this paper describes about more relevant features which will profile the authors more accurately. Readability metrics, vocabulary richness, and emotional status are the features which are taken into consideration. Age and gender are detected as the metrics for author profiling. Stylometry is defined by using deep learning algorithm. This approach has attained an accuracy of 97.7% for gender and 90.1% for age prediction. © Springer Nature Singapore Pte Ltd. 2017.

Cite this Research Publication : K. Surendran, Harilal, O. P., Hrudya, P., Poornachandran, P., and Suchetha, N. K., “Stylometry detection using deep learning”, Advances in Intelligent Systems and Computing, vol. 556, pp. 749-757, 2017.

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