@article{Weren_Kauer_Mizusaki_Moreira_de Oliveira_Wives_2014, title={Examining Multiple Features for Author Profiling}, volume={5}, url={https://sol.sbc.org.br/journals/index.php/jidm/article/view/1543}, DOI={10.5753/jidm.2014.1543}, abstractNote={Authorship analysis aims at classifying texts based on the stylistic choices of their authors. <br />The idea is to discover characteristics of the authors of the texts. <br />This task has a growing importance in forensics, security, and marketing. <br />In this work, we focus on discovering age and gender from blog authors. <br />With this goal in mind, we analyzed a large number of features -- ranging from Information Retrieval to Sentiment Analysis. <br />This paper reports on the usefulness of these features. <br />Experiments on a corpus of over 236K blogs show that a classifier using the features explored here have outperformed the state-of-the art.<br />More importantly, the experiments show that the Information Retrieval features proposed in our work are the most discriminative and yield the best class predictions.}, number={3}, journal={Journal of Information and Data Management}, author={Weren, Edson R. D. and Kauer, Anderson U. and Mizusaki, Lucas and Moreira, Viviane P. and de Oliveira, J. Palazzo M. and Wives, Leandro K.}, year={2014}, month={Oct.}, pages={266} }