IOH-RCNN: Pursuing the ingredients of happiness using recurrent convolutional neural networks

Modeling human affect is non-trivial. To undertake this challenge, a novel shared task focusing on happiness is organized at an AAAI workshop. The CL-AFF Shared Task, titled “In Pursuit of Happiness”, consists of two sub-tasks on a dataset of descriptions of happy moments (taken from the HappyDB dataset), each annotated with individuals' demographics, recollection time and relevant labels. We focus on the first sub-task, which is a semi-supervised task to determine a happy moment's agency and social label. We present a deep learning system for this task based on Recurrent Convolutional Neural Networks (RCNN). The presented system (which we call IoH-RCNN) is trained and tested on the available dataset using 10-fold cross-validation. For predicting the agency label, the average accuracy, f1 and AUC are 85.5, 90.3 and 80.0, respectively. As for predicting the social label, the average accuracy, f1 and AUC are 91.8, 92.2 and 91.2, respectively.