Gender identification on Twitter
研究了在推特文本中识别作者性别的多种机器学习模型，提出一种两阶段特征选择方法，可将特征数量减少到几百个而不显著降低性能，并分析了最具区分性的词汇。
Abstract To determine the author of a text's gender, various feature types have been suggested (e.g., function words, n ‐gram of letters, etc.) leading to a huge number of stylistic markers. To determine the target category, different machine learning models have been suggested (e.g., logistic regression, decision tree, k nearest‐neighbors, support vector machine, naïve Bayes, neural networks, and random forest). In this study, our first objective is to know whether or not the same model always proposes the best effectiveness when considering similar corpora under the same conditions. Thus, based on 7 CLEF‐PAN collections, this study analyzes the effectiveness of 10 different classifiers. Our second aim is to propose a 2‐stage feature selection to reduce the feature size to a few hundred terms without any significant change in the performance level compared to approaches using all the attributes (increase of around 5% after applying the proposed feature selection). Based on our experiments, neural network or random forest tend, on average, to produce the highest effectiveness. Moreover, empirical evidence indicates that reducing the feature set size to around 300 without penalizing the effectiveness is possible. Finally, based on such reduced feature sizes, an analysis reveals some of the specific terms that clearly discriminate between the 2 genders.