Affective Content Classification using Convolutional Neural Networks


We present a three-layer convolutional neural network for the classification of two binary target variables 'Social' and 'Agency' in the HappyDB corpus exploiting lexical density of a closed domain and a high degree of regularity in linguistic patterns. Incorporating demographic information is demonstrated to improve classification accuracy. Custom embeddings learned from additional unlabeled data perform competitive to established pre-trained models based on much more comprehensive general training corpora. The top-performing model achieves accuracies of 0.90 for the 'Social' and 0.875 for the 'Agency' variable

Similar works

Full text



Full text is not available time updated on 4/30/2019

This paper was published in Fraunhofer-ePrints.

Having an issue?

Is data on this page outdated, violates copyrights or anything else? Report the problem now and we will take corresponding actions after reviewing your request.