Publication Type:

Journal Article


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





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


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.


cited By 0; Conference of 3rd International Conference on Computational Intelligence in Data Mining, ICCIDM 2016 ; Conference Date: 10 December 2016 Through 11 December 2016; Conference Code:192149

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.