  The latent knowledge in the emotions and the opinions of the individuals that
are manifested via social networks are crucial to numerous applications
including social management, dynamical processes, and public security.
Affective computing, as an interdisciplinary research field, linking artificial
intelligence to cognitive inference, is capable to exploit emotion-oriented
knowledge from brief contents. The textual contents convey hidden information
such as personality and cognition about corresponding authors that can
determine both correlations and variations between users. Emotion recognition
from brief contents should embrace the contrast between authors where the
differences in personality and cognition can be traced within emotional
expressions. To tackle this challenge, we devise a framework that, on the one
hand, infers latent individual aspects, from brief contents and, on the other
hand, presents a novel ensemble classifier equipped with dynamic dropout
convnets to extract emotions from textual context. To categorize short text
contents, our proposed method conjointly leverages cognitive factors and
exploits hidden information. We utilize the outcome vectors in a novel
embedding model to foster emotion-pertinent features that are collectively
assembled by lexicon inductions. Experimental results show that compared to
other competitors, our proposed model can achieve a higher performance in
recognizing emotion from noisy contents.
