[Cl-AFF shared task] modeling happiness using one-class autoencoders

Abstract

In this paper, we present a semi-supervised approach to modeling social and agentic characteristics of happiness. For this, we build four one-class autoencoder models, respectivley trained with 1) only social, 2) non-social, 3) agentic, and 4) non-agentic happiness. Then, we extract data from unlabeled data that are likely to belong to a prescribed type, as determined by the models. This paper presents the performance of predicting agency and social class with and without the extracted data. Our evaluation shows that the results are promising. © 2019 CEUR-WS. All rights reserved.