Word unigram weighing for author profiling at PAN 2018 : notebook for PAN at CLEF 2018
Pius von Däniken, Ralf Grubenmann, Mark Cieliebak · Zürcher Hochschule für Angewandte Wissenschaften digital collection (Zurich University of Applied Sciences) · 2018
We present our system for the author profiling task at PAN 2018 on gender identification on Twitter.The submitted system uses word unigrams, character 1to 5-grams and emoji unigrams as features to train a logistic regression classifier.We explore the impact of three different word unigram weighing schemes on our system's performance.Our submission achieved accuracies of 77.42% for English, 74.64% for Spanish, and 73.20% for Arabic tweets.It ranked 15th out of 23 competitors.