Reusable workflows for gender prediction

Abstract

This paper presents a system for author profiling (AP) modeling that reduces the complexity and time of building a sophisticated model for a number of different AP tasks. The system is implemented in a cloud-based visual programming platform ClowdFlows and is publicly available to a wider audience. In the platform, we also implemented our already existing state of the art gender prediction model and tested it on a number of cross-genre tasks. The results show that the implemented model, which was trained on tweets, achieves results comparable to state of the art models for cross-genre gender prediction. There is however a noticeable decrease in accuracy when the genre of a test set is different from the genre of the train set.

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