Gender and language-variety Identification with MicroTC

In this notebook, we describe our approach to cope with the Author Profiling task on PAN17 which consists of both gender and language identification for Twitter’s users. We used our MicroTC (μTC) framework as the primary tool to create our classifiers. μTC follows a simple approach to text classification; it converts the problem of text classification to a model selection problem using several simple text transformations, a combination of tokenizers, a term-weighting scheme, and finally, it classifies using a Support Vector Machine. Our approach reaches accuracies of 0.7838, 0.8054, 0.7957, and 0.8538, for gender identification; and for language variety, it achieves 0.8275, 0.9004, 0.9554, and 0.9850. All these, for Arabic, English, Spanish, and Portuguese languages, respectively.