TY - GEN

T1 - Predicting the age of social network users from user-generated texts with word embeddings

AU - Alekseev, Anton

AU - Nikolenko, Sergey I.

PY - 2017/4/3

Y1 - 2017/4/3

N2 - Many web-based applications such as advertising or recommender systems often critically depend on the demographic information, which may be unavailable for new or anonymous users. We study the problem of predicting demographic information based on user-generated texts on a Russian-language dataset from a large social network. We evaluate the efficiency of age prediction algorithms based on word2vec word embeddings and conduct a comprehensive experimental evaluation, comparing these algorithms with each other and with classical baseline approaches.

AB - Many web-based applications such as advertising or recommender systems often critically depend on the demographic information, which may be unavailable for new or anonymous users. We study the problem of predicting demographic information based on user-generated texts on a Russian-language dataset from a large social network. We evaluate the efficiency of age prediction algorithms based on word2vec word embeddings and conduct a comprehensive experimental evaluation, comparing these algorithms with each other and with classical baseline approaches.

UR - http://www.scopus.com/inward/record.url?scp=85018445292&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85018445292

SN - 978-952683978-3

BT - Proceedings of the AINL FRUCT 2016 Conference

T2 - 5th Artificial Intelligence and Natural Language FRUCT Conference, AINL FRUCT 2016

Y2 - 10 November 2016 through 12 November 2016

ER -