  We present our system, CruzAffect, for the CL-Aff Shared Task 2019.
CruzAffect consists of several types of robust and efficient models for
affective classification tasks. We utilize both traditional classifiers, such
as XGBoosted Forest, as well as a deep learning Convolutional Neural Networks
(CNN) classifier. We explore rich feature sets such as syntactic features,
emotional features, and profile features, and utilize several sentiment
lexicons, to discover essential indicators of social involvement and control
that a subject might exercise in their happy moments, as described in textual
snippets from the HappyDB database. The data comes with a labeled set (10K),
and a larger unlabeled set (70K). We therefore use supervised methods on the
10K dataset, and a bootstrapped semi-supervised approach for the 70K. We
evaluate these models for binary classification of agency and social labels
(Task 1), as well as multi-class prediction for concepts labels (Task 2). We
obtain promising results on the held-out data, suggesting that the proposed
feature sets effectively represent the data for affective classification tasks.
We also build concepts models that discover general themes recurring in happy
moments. Our results indicate that generic characteristics are shared between
the classes of agency, social and concepts, suggesting it should be possible to
build general models for affective classification tasks.
